mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
Add MatchaTTS for the Chinese dataset Baker (#1849)
This commit is contained in:
Fangjun Kuang
GitHub
parent
df46a3eaf9
commit
bfffda5afb
167
.github/scripts/baker_zh/TTS/run-matcha.sh
vendored
Executable file
167
.github/scripts/baker_zh/TTS/run-matcha.sh
vendored
Executable file
@ -0,0 +1,167 @@
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#!/usr/bin/env bash
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set -ex
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apt-get update
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apt-get install -y sox
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python3 -m pip install numba conformer==0.3.2 diffusers librosa
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python3 -m pip install jieba
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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cd egs/baker_zh/TTS
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sed -i.bak s/600/8/g ./prepare.sh
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sed -i.bak s/"first 100"/"first 3"/g ./prepare.sh
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sed -i.bak s/500/5/g ./prepare.sh
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git diff
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function prepare_data() {
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# We have created a subset of the data for testing
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#
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mkdir -p download
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pushd download
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wget -q https://huggingface.co/csukuangfj/tmp-files/resolve/main/BZNSYP-samples.tar.bz2
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tar xvf BZNSYP-samples.tar.bz2
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mv BZNSYP-samples BZNSYP
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rm BZNSYP-samples.tar.bz2
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popd
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./prepare.sh
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tree .
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}
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function train() {
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pushd ./matcha
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sed -i.bak s/1500/3/g ./train.py
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git diff .
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popd
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./matcha/train.py \
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--exp-dir matcha/exp \
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--num-epochs 1 \
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--save-every-n 1 \
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--num-buckets 2 \
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--tokens data/tokens.txt \
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--max-duration 20
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ls -lh matcha/exp
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}
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function infer() {
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
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./matcha/infer.py \
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--num-buckets 2 \
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--epoch 1 \
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--exp-dir ./matcha/exp \
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--tokens data/tokens.txt \
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--cmvn ./data/fbank/cmvn.json \
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--vocoder ./generator_v2 \
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--input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
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--output-wav ./generated.wav
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ls -lh *.wav
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soxi ./generated.wav
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rm -v ./generated.wav
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rm -v generator_v2
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}
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function export_onnx() {
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pushd matcha/exp
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/epoch-2000.pt
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popd
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pushd data/fbank
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rm -v *.json
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/cmvn.json
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popd
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./matcha/export_onnx.py \
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--exp-dir ./matcha/exp \
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--epoch 2000 \
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--tokens ./data/tokens.txt \
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--cmvn ./data/fbank/cmvn.json
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ls -lh *.onnx
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if false; then
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# The CI machine does not have enough memory to run it
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#
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
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python3 ./matcha/export_onnx_hifigan.py
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else
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v1.onnx
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v2.onnx
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v3.onnx
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fi
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ls -lh *.onnx
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python3 ./matcha/generate_lexicon.py
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for v in v1 v2 v3; do
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python3 ./matcha/onnx_pretrained.py \
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--acoustic-model ./model-steps-6.onnx \
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--vocoder ./hifigan_$v.onnx \
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--tokens ./data/tokens.txt \
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--lexicon ./lexicon.txt \
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--input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
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--output-wav /icefall/generated-matcha-tts-steps-6-$v.wav
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done
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ls -lh /icefall/*.wav
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soxi /icefall/generated-matcha-tts-steps-6-*.wav
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cp ./model-steps-*.onnx /icefall
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d=matcha-icefall-zh-baker
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mkdir $d
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cp -v data/tokens.txt $d
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cp -v lexicon.txt $d
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cp model-steps-3.onnx $d
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pushd $d
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curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2
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tar xvf dict.tar.bz2
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rm dict.tar.bz2
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/date.fst
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/number.fst
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curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/phone.fst
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cat >README.md <<EOF
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# Introduction
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This model is trained using the dataset from
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https://en.data-baker.com/datasets/freeDatasets/
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The dataset contains 10000 Chinese sentences of a native Chinese female speaker,
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which is about 12 hours.
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**Note**: The dataset is for non-commercial use only.
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You can find the training code at
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https://github.com/k2-fsa/icefall/tree/master/egs/baker_zh/TTS
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EOF
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ls -lh
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popd
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tar cvjf $d.tar.bz2 $d
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mv $d.tar.bz2 /icefall
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mv $d /icefall
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}
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prepare_data
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train
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infer
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export_onnx
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rm -rfv generator_v* matcha/exp
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git checkout .
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18
.github/scripts/docker/generate_build_matrix.py
vendored
18
.github/scripts/docker/generate_build_matrix.py
vendored
@ -2,9 +2,19 @@
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
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import argparse
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import json
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--min-torch-version",
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help="Minimu torch version",
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)
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return parser.parse_args()
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def version_gt(a, b):
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a_major, a_minor = list(map(int, a.split(".")))[:2]
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b_major, b_minor = list(map(int, b.split(".")))[:2]
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@ -42,7 +52,7 @@ def get_torchaudio_version(torch_version):
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return torch_version
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def get_matrix():
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def get_matrix(min_torch_version):
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k2_version = "1.24.4.dev20241029"
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kaldifeat_version = "1.25.5.dev20241029"
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version = "20241218"
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@ -64,6 +74,9 @@ def get_matrix():
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matrix = []
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for p in python_version:
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for t in torch_version:
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if min_torch_version and version_gt(min_torch_version, t):
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continue
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# torchaudio <= 1.13.x supports only python <= 3.10
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if version_gt(p, "3.10") and not version_gt(t, "2.0"):
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@ -101,7 +114,8 @@ def get_matrix():
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def main():
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matrix = get_matrix()
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args = get_args()
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matrix = get_matrix(min_torch_version=args.min_torch_version)
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print(json.dumps({"include": matrix}))
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2
.github/scripts/ljspeech/TTS/run-matcha.sh
vendored
2
.github/scripts/ljspeech/TTS/run-matcha.sh
vendored
@ -90,7 +90,7 @@ function export_onnx() {
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ls -lh *.onnx
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if false; then
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# THe CI machine does not have enough memory to run it
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# The CI machine does not have enough memory to run it
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#
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
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curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
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152
.github/workflows/baker_zh.yml
vendored
Normal file
152
.github/workflows/baker_zh.yml
vendored
Normal file
@ -0,0 +1,152 @@
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name: baker_zh
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on:
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push:
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branches:
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- master
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- baker-matcha-2
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pull_request:
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branches:
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- master
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workflow_dispatch:
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concurrency:
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group: baker-zh-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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generate_build_matrix:
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if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
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# see https://github.com/pytorch/pytorch/pull/50633
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runs-on: ubuntu-latest
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outputs:
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matrix: ${{ steps.set-matrix.outputs.matrix }}
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Generating build matrix
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id: set-matrix
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run: |
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# outputting for debugging purposes
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python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
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MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
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echo "::set-output name=matrix::${MATRIX}"
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baker_zh:
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needs: generate_build_matrix
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name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Free space
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shell: bash
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run: |
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ls -lh
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df -h
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rm -rf /opt/hostedtoolcache
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df -h
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echo "pwd: $PWD"
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echo "github.workspace ${{ github.workspace }}"
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- name: Run tests
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uses: addnab/docker-run-action@v3
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with:
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image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
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options: |
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--volume ${{ github.workspace }}/:/icefall
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shell: bash
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run: |
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export PYTHONPATH=/icefall:$PYTHONPATH
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cd /icefall
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pip install onnx==1.17.0
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pip list
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git config --global --add safe.directory /icefall
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.github/scripts/baker_zh/TTS/run-matcha.sh
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- name: display files
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shell: bash
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run: |
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ls -lh
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- uses: actions/upload-artifact@v4
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
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with:
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name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
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path: ./*.wav
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- uses: actions/upload-artifact@v4
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
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with:
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name: step-2
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path: ./model-steps-2.onnx
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- uses: actions/upload-artifact@v4
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
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with:
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name: step-3
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path: ./model-steps-3.onnx
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- uses: actions/upload-artifact@v4
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
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with:
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name: step-4
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path: ./model-steps-4.onnx
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|
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- uses: actions/upload-artifact@v4
|
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
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with:
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name: step-5
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path: ./model-steps-5.onnx
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|
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- uses: actions/upload-artifact@v4
|
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
|
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with:
|
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name: step-6
|
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path: ./model-steps-6.onnx
|
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|
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- name: Upload models to huggingface
|
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if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
|
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shell: bash
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env:
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HF_TOKEN: ${{ secrets.HF_TOKEN }}
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run: |
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d=matcha-icefall-zh-baker
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|
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/$d hf
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cp -av $d/* hf/
|
||||
|
||||
pushd hf
|
||||
git add .
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||||
|
||||
git config --global user.name "csukuangfj"
|
||||
git config --global user.email "csukuangfj@gmail.com"
|
||||
git config --global lfs.allowincompletepush true
|
||||
|
||||
git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$d main || true
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popd
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|
||||
- name: Release exported onnx models
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
|
||||
uses: svenstaro/upload-release-action@v2
|
||||
with:
|
||||
file_glob: true
|
||||
overwrite: true
|
||||
file: matcha-icefall-*.tar.bz2
|
||||
repo_name: k2-fsa/sherpa-onnx
|
||||
repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
|
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tag: tts-models
|
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6
egs/baker_zh/TTS/.gitignore
vendored
Normal file
6
egs/baker_zh/TTS/.gitignore
vendored
Normal file
@ -0,0 +1,6 @@
|
||||
path.sh
|
||||
*.onnx
|
||||
*.wav
|
||||
generator_v1
|
||||
generator_v2
|
||||
generator_v3
|
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146
egs/baker_zh/TTS/README.md
Normal file
146
egs/baker_zh/TTS/README.md
Normal file
@ -0,0 +1,146 @@
|
||||
# Introduction
|
||||
|
||||
It is for the dataset from
|
||||
https://en.data-baker.com/datasets/freeDatasets/
|
||||
|
||||
The dataset contains 10000 Chinese sentences of a native Chinese female speaker,
|
||||
which is about 12 hours.
|
||||
|
||||
|
||||
**Note**: The dataset is for non-commercial use only.
|
||||
|
||||
|
||||
# matcha
|
||||
|
||||
[./matcha](./matcha) contains the code for training [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS)
|
||||
|
||||
Checkpoints and training logs can be found [here](https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27).
|
||||
The pull-request for this recipe can be found at <https://github.com/k2-fsa/icefall/pull/1849>
|
||||
|
||||
The training command is given below:
|
||||
```bash
|
||||
python3 ./matcha/train.py \
|
||||
--exp-dir ./matcha/exp-1/ \
|
||||
--num-workers 4 \
|
||||
--world-size 1 \
|
||||
--num-epochs 2000 \
|
||||
--max-duration 1200 \
|
||||
--bucketing-sampler 1 \
|
||||
--start-epoch 1
|
||||
```
|
||||
|
||||
To inference, use:
|
||||
|
||||
```bash
|
||||
# Download Hifigan vocoder. We use Hifigan v2 below. You can select from v1, v2, or v3
|
||||
|
||||
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
|
||||
|
||||
python3 ./matcha/infer.py \
|
||||
--epoch 2000 \
|
||||
--exp-dir ./matcha/exp-1 \
|
||||
--vocoder ./generator_v2 \
|
||||
--tokens ./data/tokens.txt \
|
||||
--cmvn ./data/fbank/cmvn.json \
|
||||
--input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
|
||||
--output-wav ./generated.wav
|
||||
```
|
||||
|
||||
```bash
|
||||
soxi ./generated.wav
|
||||
```
|
||||
|
||||
prints:
|
||||
```
|
||||
Input File : './generated.wav'
|
||||
Channels : 1
|
||||
Sample Rate : 22050
|
||||
Precision : 16-bit
|
||||
Duration : 00:00:17.31 = 381696 samples ~ 1298.29 CDDA sectors
|
||||
File Size : 763k
|
||||
Bit Rate : 353k
|
||||
Sample Encoding: 16-bit Signed Integer PCM
|
||||
```
|
||||
|
||||
https://github.com/user-attachments/assets/88d4e88f-ebc4-4f32-b216-16d46b966024
|
||||
|
||||
|
||||
To export the checkpoint to onnx:
|
||||
```bash
|
||||
python3 ./matcha/export_onnx.py \
|
||||
--exp-dir ./matcha/exp-1 \
|
||||
--epoch 2000 \
|
||||
--tokens ./data/tokens.txt \
|
||||
--cmvn ./data/fbank/cmvn.json
|
||||
```
|
||||
|
||||
The above command generates the following files:
|
||||
```
|
||||
-rw-r--r-- 1 kuangfangjun root 72M Dec 27 18:53 model-steps-2.onnx
|
||||
-rw-r--r-- 1 kuangfangjun root 73M Dec 27 18:54 model-steps-3.onnx
|
||||
-rw-r--r-- 1 kuangfangjun root 73M Dec 27 18:54 model-steps-4.onnx
|
||||
-rw-r--r-- 1 kuangfangjun root 74M Dec 27 18:55 model-steps-5.onnx
|
||||
-rw-r--r-- 1 kuangfangjun root 74M Dec 27 18:57 model-steps-6.onnx
|
||||
```
|
||||
|
||||
where the 2 in `model-steps-2.onnx` means it uses 2 steps for the ODE solver.
|
||||
|
||||
**HINT**: If you get the following error while running `export_onnx.py`:
|
||||
|
||||
```
|
||||
torch.onnx.errors.UnsupportedOperatorError: Exporting the operator
|
||||
'aten::scaled_dot_product_attention' to ONNX opset version 14 is not supported.
|
||||
```
|
||||
|
||||
please use `torch>=2.2.0`.
|
||||
|
||||
To export the Hifigan vocoder to onnx, please use:
|
||||
|
||||
```bash
|
||||
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
|
||||
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
|
||||
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
|
||||
|
||||
python3 ./matcha/export_onnx_hifigan.py
|
||||
```
|
||||
|
||||
The above command generates 3 files:
|
||||
|
||||
- hifigan_v1.onnx
|
||||
- hifigan_v2.onnx
|
||||
- hifigan_v3.onnx
|
||||
|
||||
**HINT**: You can download pre-exported hifigan ONNX models from
|
||||
<https://github.com/k2-fsa/sherpa-onnx/releases/tag/vocoder-models>
|
||||
|
||||
To use the generated onnx files to generate speech from text, please run:
|
||||
|
||||
```bash
|
||||
|
||||
# First, generate ./lexicon.txt
|
||||
python3 ./matcha/generate_lexicon.py
|
||||
|
||||
python3 ./matcha/onnx_pretrained.py \
|
||||
--acoustic-model ./model-steps-4.onnx \
|
||||
--vocoder ./hifigan_v2.onnx \
|
||||
--tokens ./data/tokens.txt \
|
||||
--lexicon ./lexicon.txt \
|
||||
--input-text "在一个阳光明媚的夏天，小马、小羊和小狗它们一块儿在广阔的草地上，嬉戏玩耍，这时小猴来了，还带着它心爱的足球活蹦乱跳地跑前、跑后教小马、小羊、小狗踢足球。" \
|
||||
--output-wav ./1.wav
|
||||
```
|
||||
|
||||
```bash
|
||||
soxi ./1.wav
|
||||
|
||||
Input File : './1.wav'
|
||||
Channels : 1
|
||||
Sample Rate : 22050
|
||||
Precision : 16-bit
|
||||
Duration : 00:00:16.37 = 360960 samples ~ 1227.76 CDDA sectors
|
||||
File Size : 722k
|
||||
Bit Rate : 353k
|
||||
Sample Encoding: 16-bit Signed Integer PCM
|
||||
```
|
||||
|
||||
https://github.com/user-attachments/assets/578d04bb-fee8-47e5-9984-a868dcce610e
|
||||
|
||||
1
egs/baker_zh/TTS/local/audio.py
Symbolic link
1
egs/baker_zh/TTS/local/audio.py
Symbolic link
@ -0,0 +1 @@
|
||||
../matcha/audio.py
|
||||
110
egs/baker_zh/TTS/local/compute_fbank_baker_zh.py
Executable file
110
egs/baker_zh/TTS/local/compute_fbank_baker_zh.py
Executable file
@ -0,0 +1,110 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
"""
|
||||
This file computes fbank features of the baker-zh dataset.
|
||||
It looks for manifests in the directory data/manifests.
|
||||
|
||||
The generated fbank features are saved in data/fbank.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from fbank import MatchaFbank, MatchaFbankConfig
|
||||
from lhotse import CutSet, LilcomChunkyWriter, load_manifest
|
||||
from lhotse.audio import RecordingSet
|
||||
from lhotse.supervision import SupervisionSet
|
||||
|
||||
from icefall.utils import get_executor
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-jobs",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
""",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
def compute_fbank_baker_zh(num_jobs: int):
|
||||
src_dir = Path("data/manifests")
|
||||
output_dir = Path("data/fbank")
|
||||
|
||||
if num_jobs < 1:
|
||||
num_jobs = os.cpu_count()
|
||||
|
||||
logging.info(f"num_jobs: {num_jobs}")
|
||||
logging.info(f"src_dir: {src_dir}")
|
||||
logging.info(f"output_dir: {output_dir}")
|
||||
config = MatchaFbankConfig(
|
||||
n_fft=1024,
|
||||
n_mels=80,
|
||||
sampling_rate=22050,
|
||||
hop_length=256,
|
||||
win_length=1024,
|
||||
f_min=0,
|
||||
f_max=8000,
|
||||
)
|
||||
|
||||
prefix = "baker_zh"
|
||||
suffix = "jsonl.gz"
|
||||
|
||||
extractor = MatchaFbank(config)
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
cuts_filename = f"{prefix}_cuts.{suffix}"
|
||||
logging.info(f"Processing {cuts_filename}")
|
||||
cut_set = load_manifest(src_dir / cuts_filename).resample(22050)
|
||||
|
||||
cut_set = cut_set.compute_and_store_features(
|
||||
extractor=extractor,
|
||||
storage_path=f"{output_dir}/{prefix}_feats",
|
||||
num_jobs=num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
storage_type=LilcomChunkyWriter,
|
||||
)
|
||||
|
||||
cut_set.to_file(output_dir / cuts_filename)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Torch's multithreaded behavior needs to be disabled or
|
||||
# it wastes a lot of CPU and slow things down.
|
||||
# Do this outside of main() in case it needs to take effect
|
||||
# even when we are not invoking the main (e.g. when spawning subprocesses).
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
|
||||
args = get_parser().parse_args()
|
||||
compute_fbank_baker_zh(args.num_jobs)
|
||||
1
egs/baker_zh/TTS/local/compute_fbank_statistics.py
Symbolic link
1
egs/baker_zh/TTS/local/compute_fbank_statistics.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/local/compute_fbank_statistics.py
|
||||
121
egs/baker_zh/TTS/local/convert_text_to_tokens.py
Executable file
121
egs/baker_zh/TTS/local/convert_text_to_tokens.py
Executable file
@ -0,0 +1,121 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import argparse
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
import jieba
|
||||
from lhotse import load_manifest
|
||||
from pypinyin import Style, lazy_pinyin, load_phrases_dict
|
||||
|
||||
load_phrases_dict(
|
||||
{
|
||||
"行长": [["hang2"], ["zhang3"]],
|
||||
"银行行长": [["yin2"], ["hang2"], ["hang2"], ["zhang3"]],
|
||||
}
|
||||
)
|
||||
|
||||
whiter_space_re = re.compile(r"\s+")
|
||||
|
||||
punctuations_re = [
|
||||
(re.compile(x[0], re.IGNORECASE), x[1])
|
||||
for x in [
|
||||
("，", ","),
|
||||
("。", "."),
|
||||
("！", "!"),
|
||||
("？", "?"),
|
||||
("“", '"'),
|
||||
("”", '"'),
|
||||
("‘", "'"),
|
||||
("’", "'"),
|
||||
("：", ":"),
|
||||
("、", ","),
|
||||
("Ｂ", "逼"),
|
||||
("Ｐ", "批"),
|
||||
]
|
||||
]
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument(
|
||||
"--in-file",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Input cutset.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--out-file",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Output cutset.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def normalize_white_spaces(text):
|
||||
return whiter_space_re.sub(" ", text)
|
||||
|
||||
|
||||
def normalize_punctuations(text):
|
||||
for regex, replacement in punctuations_re:
|
||||
text = re.sub(regex, replacement, text)
|
||||
return text
|
||||
|
||||
|
||||
def split_text(text: str) -> List[str]:
|
||||
"""
|
||||
Example input: '你好呀，You are 一个好人。 去银行存钱？How about you?'
|
||||
Example output: ['你好', '呀', ',', 'you are', '一个', '好人', '.', '去', '银行', '存钱', '?', 'how about you', '?']
|
||||
"""
|
||||
text = text.lower()
|
||||
text = normalize_white_spaces(text)
|
||||
text = normalize_punctuations(text)
|
||||
ans = []
|
||||
|
||||
for seg in jieba.cut(text):
|
||||
if seg in ",.!?:\"'":
|
||||
ans.append(seg)
|
||||
elif seg == " " and len(ans) > 0:
|
||||
if ord("a") <= ord(ans[-1][-1]) <= ord("z"):
|
||||
ans[-1] += seg
|
||||
elif ord("a") <= ord(seg[0]) <= ord("z"):
|
||||
if len(ans) == 0:
|
||||
ans.append(seg)
|
||||
continue
|
||||
|
||||
if ans[-1][-1] == " ":
|
||||
ans[-1] += seg
|
||||
continue
|
||||
|
||||
ans.append(seg)
|
||||
else:
|
||||
ans.append(seg)
|
||||
|
||||
ans = [s.strip() for s in ans]
|
||||
return ans
|
||||
|
||||
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
cuts = load_manifest(args.in_file)
|
||||
for c in cuts:
|
||||
assert len(c.supervisions) == 1, (len(c.supervisions), c.supervisions)
|
||||
text = c.supervisions[0].normalized_text
|
||||
|
||||
text_list = split_text(text)
|
||||
tokens = lazy_pinyin(text_list, style=Style.TONE3, tone_sandhi=True)
|
||||
|
||||
c.tokens = tokens
|
||||
|
||||
cuts.to_file(args.out_file)
|
||||
|
||||
print(f"saved to {args.out_file}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
egs/baker_zh/TTS/local/fbank.py
Symbolic link
1
egs/baker_zh/TTS/local/fbank.py
Symbolic link
@ -0,0 +1 @@
|
||||
../matcha/fbank.py
|
||||
85
egs/baker_zh/TTS/local/generate_tokens.py
Executable file
85
egs/baker_zh/TTS/local/generate_tokens.py
Executable file
@ -0,0 +1,85 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
"""
|
||||
This file generates the file tokens.txt.
|
||||
|
||||
Usage:
|
||||
|
||||
python3 ./local/generate_tokens.py > data/tokens.txt
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
from typing import List
|
||||
|
||||
import jieba
|
||||
from pypinyin import Style, lazy_pinyin, pinyin_dict
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tokens",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to to save tokens.txt.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def generate_token_list() -> List[str]:
|
||||
token_set = set()
|
||||
|
||||
word_dict = pinyin_dict.pinyin_dict
|
||||
i = 0
|
||||
for key in word_dict:
|
||||
if not (0x4E00 <= key <= 0x9FFF):
|
||||
continue
|
||||
|
||||
w = chr(key)
|
||||
t = lazy_pinyin(w, style=Style.TONE3, tone_sandhi=True)[0]
|
||||
token_set.add(t)
|
||||
|
||||
no_digit = set()
|
||||
for t in token_set:
|
||||
if t[-1] not in "1234":
|
||||
no_digit.add(t)
|
||||
else:
|
||||
no_digit.add(t[:-1])
|
||||
|
||||
no_digit.add("dei")
|
||||
no_digit.add("tou")
|
||||
no_digit.add("dia")
|
||||
|
||||
for t in no_digit:
|
||||
token_set.add(t)
|
||||
for i in range(1, 5):
|
||||
token_set.add(f"{t}{i}")
|
||||
|
||||
ans = list(token_set)
|
||||
ans.sort()
|
||||
|
||||
punctuations = list(",.!?:\"'")
|
||||
ans = punctuations + ans
|
||||
|
||||
# use ID 0 for blank
|
||||
# Use ID 1 of _ for padding
|
||||
ans.insert(0, " ")
|
||||
ans.insert(1, "_") #
|
||||
|
||||
return ans
|
||||
|
||||
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
token_list = generate_token_list()
|
||||
with open(args.tokens, "w", encoding="utf-8") as f:
|
||||
for indx, token in enumerate(token_list):
|
||||
f.write(f"{token} {indx}\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
70
egs/baker_zh/TTS/local/validate_manifest.py
Executable file
70
egs/baker_zh/TTS/local/validate_manifest.py
Executable file
@ -0,0 +1,70 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022-2023 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
This script checks the following assumptions of the generated manifest:
|
||||
|
||||
- Single supervision per cut
|
||||
|
||||
We will add more checks later if needed.
|
||||
|
||||
Usage example:
|
||||
|
||||
python3 ./local/validate_manifest.py \
|
||||
./data/spectrogram/baker_zh_cuts_all.jsonl.gz
|
||||
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
from lhotse import CutSet, load_manifest_lazy
|
||||
from lhotse.dataset.speech_synthesis import validate_for_tts
|
||||
|
||||
|
||||
def get_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument(
|
||||
"manifest",
|
||||
type=Path,
|
||||
help="Path to the manifest file",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
args = get_args()
|
||||
|
||||
manifest = args.manifest
|
||||
logging.info(f"Validating {manifest}")
|
||||
|
||||
assert manifest.is_file(), f"{manifest} does not exist"
|
||||
cut_set = load_manifest_lazy(manifest)
|
||||
assert isinstance(cut_set, CutSet), type(cut_set)
|
||||
|
||||
validate_for_tts(cut_set)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
|
||||
main()
|
||||
0
egs/baker_zh/TTS/matcha/__init__.py
Normal file
0
egs/baker_zh/TTS/matcha/__init__.py
Normal file
1
egs/baker_zh/TTS/matcha/audio.py
Symbolic link
1
egs/baker_zh/TTS/matcha/audio.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/audio.py
|
||||
207
egs/baker_zh/TTS/matcha/export_onnx.py
Executable file
207
egs/baker_zh/TTS/matcha/export_onnx.py
Executable file
@ -0,0 +1,207 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
|
||||
"""
|
||||
This script exports a Matcha-TTS model to ONNX.
|
||||
Note that the model outputs fbank. You need to use a vocoder to convert
|
||||
it to audio. See also ./export_onnx_hifigan.py
|
||||
|
||||
python3 ./matcha/export_onnx.py \
|
||||
--exp-dir ./matcha/exp-1 \
|
||||
--epoch 2000 \
|
||||
--tokens ./data/tokens.txt \
|
||||
--cmvn ./data/fbank/cmvn.json
|
||||
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict
|
||||
|
||||
import onnx
|
||||
import torch
|
||||
from tokenizer import Tokenizer
|
||||
from train import get_model, get_params
|
||||
|
||||
from icefall.checkpoint import load_checkpoint
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=Path,
|
||||
default="matcha/exp-new-3",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tokens",
|
||||
type=Path,
|
||||
default="data/tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--cmvn",
|
||||
type=str,
|
||||
default="data/fbank/cmvn.json",
|
||||
help="""Path to vocabulary.""",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def add_meta_data(filename: str, meta_data: Dict[str, Any]):
|
||||
"""Add meta data to an ONNX model. It is changed in-place.
|
||||
|
||||
Args:
|
||||
filename:
|
||||
Filename of the ONNX model to be changed.
|
||||
meta_data:
|
||||
Key-value pairs.
|
||||
"""
|
||||
model = onnx.load(filename)
|
||||
|
||||
while len(model.metadata_props):
|
||||
model.metadata_props.pop()
|
||||
|
||||
for key, value in meta_data.items():
|
||||
meta = model.metadata_props.add()
|
||||
meta.key = key
|
||||
meta.value = str(value)
|
||||
|
||||
onnx.save(model, filename)
|
||||
|
||||
|
||||
class ModelWrapper(torch.nn.Module):
|
||||
def __init__(self, model, num_steps: int = 5):
|
||||
super().__init__()
|
||||
self.model = model
|
||||
self.num_steps = num_steps
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lengths: torch.Tensor,
|
||||
noise_scale: torch.Tensor,
|
||||
length_scale: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Args: :
|
||||
x: (batch_size, num_tokens), torch.int64
|
||||
x_lengths: (batch_size,), torch.int64
|
||||
noise_scale: (1,), torch.float32
|
||||
length_scale (1,), torch.float32
|
||||
Returns:
|
||||
audio: (batch_size, num_samples)
|
||||
|
||||
"""
|
||||
mel = self.model.synthesise(
|
||||
x=x,
|
||||
x_lengths=x_lengths,
|
||||
n_timesteps=self.num_steps,
|
||||
temperature=noise_scale,
|
||||
length_scale=length_scale,
|
||||
)["mel"]
|
||||
# mel: (batch_size, feat_dim, num_frames)
|
||||
|
||||
return mel
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
params = get_params()
|
||||
|
||||
params.update(vars(args))
|
||||
|
||||
tokenizer = Tokenizer(params.tokens)
|
||||
params.pad_id = tokenizer.pad_id
|
||||
params.vocab_size = tokenizer.vocab_size
|
||||
params.model_args.n_vocab = params.vocab_size
|
||||
|
||||
with open(params.cmvn) as f:
|
||||
stats = json.load(f)
|
||||
params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.data_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
|
||||
params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.model_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_model(params)
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
|
||||
for num_steps in [2, 3, 4, 5, 6]:
|
||||
logging.info(f"num_steps: {num_steps}")
|
||||
wrapper = ModelWrapper(model, num_steps=num_steps)
|
||||
wrapper.eval()
|
||||
|
||||
# Use a large value so the rotary position embedding in the text
|
||||
# encoder has a large initial length
|
||||
x = torch.ones(1, 1000, dtype=torch.int64)
|
||||
x_lengths = torch.tensor([x.shape[1]], dtype=torch.int64)
|
||||
noise_scale = torch.tensor([1.0])
|
||||
length_scale = torch.tensor([1.0])
|
||||
|
||||
opset_version = 14
|
||||
filename = f"model-steps-{num_steps}.onnx"
|
||||
torch.onnx.export(
|
||||
wrapper,
|
||||
(x, x_lengths, noise_scale, length_scale),
|
||||
filename,
|
||||
opset_version=opset_version,
|
||||
input_names=["x", "x_length", "noise_scale", "length_scale"],
|
||||
output_names=["mel"],
|
||||
dynamic_axes={
|
||||
"x": {0: "N", 1: "L"},
|
||||
"x_length": {0: "N"},
|
||||
"mel": {0: "N", 2: "L"},
|
||||
},
|
||||
)
|
||||
|
||||
meta_data = {
|
||||
"model_type": "matcha-tts",
|
||||
"language": "Chinese",
|
||||
"has_espeak": 0,
|
||||
"n_speakers": 1,
|
||||
"jieba": 1,
|
||||
"sample_rate": 22050,
|
||||
"version": 1,
|
||||
"pad_id": params.pad_id,
|
||||
"model_author": "icefall",
|
||||
"maintainer": "k2-fsa",
|
||||
"dataset": "baker-zh",
|
||||
"use_eos_bos": 0,
|
||||
"dataset_url": "https://www.data-baker.com/open_source.html",
|
||||
"dataset_comment": "The dataset is for non-commercial use only.",
|
||||
"num_ode_steps": num_steps,
|
||||
}
|
||||
add_meta_data(filename=filename, meta_data=meta_data)
|
||||
print(meta_data)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
1
egs/baker_zh/TTS/matcha/export_onnx_hifigan.py
Symbolic link
1
egs/baker_zh/TTS/matcha/export_onnx_hifigan.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/export_onnx_hifigan.py
|
||||
1
egs/baker_zh/TTS/matcha/fbank.py
Symbolic link
1
egs/baker_zh/TTS/matcha/fbank.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/fbank.py
|
||||
42
egs/baker_zh/TTS/matcha/generate_lexicon.py
Executable file
42
egs/baker_zh/TTS/matcha/generate_lexicon.py
Executable file
@ -0,0 +1,42 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import jieba
|
||||
from pypinyin import Style, lazy_pinyin, load_phrases_dict, phrases_dict, pinyin_dict
|
||||
from tokenizer import Tokenizer
|
||||
|
||||
load_phrases_dict(
|
||||
{
|
||||
"行长": [["hang2"], ["zhang3"]],
|
||||
"银行行长": [["yin2"], ["hang2"], ["hang2"], ["zhang3"]],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
filename = "lexicon.txt"
|
||||
tokens = "./data/tokens.txt"
|
||||
tokenizer = Tokenizer(tokens)
|
||||
|
||||
word_dict = pinyin_dict.pinyin_dict
|
||||
phrases = phrases_dict.phrases_dict
|
||||
|
||||
i = 0
|
||||
with open(filename, "w", encoding="utf-8") as f:
|
||||
for key in word_dict:
|
||||
if not (0x4E00 <= key <= 0x9FFF):
|
||||
continue
|
||||
|
||||
w = chr(key)
|
||||
tokens = lazy_pinyin(w, style=Style.TONE3, tone_sandhi=True)[0]
|
||||
|
||||
f.write(f"{w} {tokens}\n")
|
||||
|
||||
for key in phrases:
|
||||
tokens = lazy_pinyin(key, style=Style.TONE3, tone_sandhi=True)
|
||||
tokens = " ".join(tokens)
|
||||
|
||||
f.write(f"{key} {tokens}\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
egs/baker_zh/TTS/matcha/hifigan
Symbolic link
1
egs/baker_zh/TTS/matcha/hifigan
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/hifigan
|
||||
342
egs/baker_zh/TTS/matcha/infer.py
Executable file
342
egs/baker_zh/TTS/matcha/infer.py
Executable file
@ -0,0 +1,342 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
"""
|
||||
python3 ./matcha/infer.py \
|
||||
--epoch 2000 \
|
||||
--exp-dir ./matcha/exp-1 \
|
||||
--vocoder ./generator_v2 \
|
||||
--tokens ./data/tokens.txt \
|
||||
--cmvn ./data/fbank/cmvn.json \
|
||||
--input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
|
||||
--output-wav ./generated.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import datetime as dt
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import soundfile as sf
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from hifigan.config import v1, v2, v3
|
||||
from hifigan.denoiser import Denoiser
|
||||
from hifigan.models import Generator as HiFiGAN
|
||||
from local.convert_text_to_tokens import split_text
|
||||
from pypinyin import Style, lazy_pinyin
|
||||
from tokenizer import Tokenizer
|
||||
from train import get_model, get_params
|
||||
from tts_datamodule import BakerZhTtsDataModule
|
||||
|
||||
from icefall.checkpoint import load_checkpoint
|
||||
from icefall.utils import AttributeDict, setup_logger
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=4000,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=Path,
|
||||
default="matcha/exp",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--vocoder",
|
||||
type=Path,
|
||||
default="./generator_v1",
|
||||
help="Path to the vocoder",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tokens",
|
||||
type=Path,
|
||||
default="data/tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--cmvn",
|
||||
type=str,
|
||||
default="data/fbank/cmvn.json",
|
||||
help="""Path to vocabulary.""",
|
||||
)
|
||||
|
||||
# The following arguments are used for inference on single text
|
||||
parser.add_argument(
|
||||
"--input-text",
|
||||
type=str,
|
||||
required=False,
|
||||
help="The text to generate speech for",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--output-wav",
|
||||
type=str,
|
||||
required=False,
|
||||
help="The filename of the wave to save the generated speech",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sampling-rate",
|
||||
type=int,
|
||||
default=22050,
|
||||
help="The sampling rate of the generated speech (default: 22050 for baker_zh)",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def load_vocoder(checkpoint_path: Path) -> nn.Module:
|
||||
checkpoint_path = str(checkpoint_path)
|
||||
if checkpoint_path.endswith("v1"):
|
||||
h = AttributeDict(v1)
|
||||
elif checkpoint_path.endswith("v2"):
|
||||
h = AttributeDict(v2)
|
||||
elif checkpoint_path.endswith("v3"):
|
||||
h = AttributeDict(v3)
|
||||
else:
|
||||
raise ValueError(f"supports only v1, v2, and v3, given {checkpoint_path}")
|
||||
|
||||
hifigan = HiFiGAN(h).to("cpu")
|
||||
hifigan.load_state_dict(
|
||||
torch.load(checkpoint_path, map_location="cpu")["generator"]
|
||||
)
|
||||
_ = hifigan.eval()
|
||||
hifigan.remove_weight_norm()
|
||||
return hifigan
|
||||
|
||||
|
||||
def to_waveform(
|
||||
mel: torch.Tensor, vocoder: nn.Module, denoiser: nn.Module
|
||||
) -> torch.Tensor:
|
||||
audio = vocoder(mel).clamp(-1, 1)
|
||||
audio = denoiser(audio.squeeze(0), strength=0.00025).cpu().squeeze()
|
||||
return audio.squeeze()
|
||||
|
||||
|
||||
def process_text(text: str, tokenizer: Tokenizer, device: str = "cpu") -> dict:
|
||||
text = split_text(text)
|
||||
tokens = lazy_pinyin(text, style=Style.TONE3, tone_sandhi=True)
|
||||
|
||||
x = tokenizer.texts_to_token_ids([tokens])
|
||||
x = torch.tensor(x, dtype=torch.long, device=device)
|
||||
x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
|
||||
return {"x_orig": text, "x": x, "x_lengths": x_lengths}
|
||||
|
||||
|
||||
def synthesize(
|
||||
model: nn.Module,
|
||||
tokenizer: Tokenizer,
|
||||
n_timesteps: int,
|
||||
text: str,
|
||||
length_scale: float,
|
||||
temperature: float,
|
||||
device: str = "cpu",
|
||||
spks=None,
|
||||
) -> dict:
|
||||
text_processed = process_text(text=text, tokenizer=tokenizer, device=device)
|
||||
start_t = dt.datetime.now()
|
||||
output = model.synthesise(
|
||||
text_processed["x"],
|
||||
text_processed["x_lengths"],
|
||||
n_timesteps=n_timesteps,
|
||||
temperature=temperature,
|
||||
spks=spks,
|
||||
length_scale=length_scale,
|
||||
)
|
||||
# merge everything to one dict
|
||||
output.update({"start_t": start_t, **text_processed})
|
||||
return output
|
||||
|
||||
|
||||
def infer_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
vocoder: nn.Module,
|
||||
denoiser: nn.Module,
|
||||
tokenizer: Tokenizer,
|
||||
) -> None:
|
||||
"""Decode dataset.
|
||||
The ground-truth and generated audio pairs will be saved to `params.save_wav_dir`.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
PyTorch's dataloader containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
tokenizer:
|
||||
Used to convert text to phonemes.
|
||||
"""
|
||||
|
||||
device = next(model.parameters()).device
|
||||
num_cuts = 0
|
||||
log_interval = 5
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
batch_size = len(batch["tokens"])
|
||||
|
||||
texts = [c.supervisions[0].normalized_text for c in batch["cut"]]
|
||||
|
||||
audio = batch["audio"]
|
||||
audio_lens = batch["audio_lens"].tolist()
|
||||
cut_ids = [cut.id for cut in batch["cut"]]
|
||||
|
||||
for i in range(batch_size):
|
||||
output = synthesize(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
n_timesteps=params.n_timesteps,
|
||||
text=texts[i],
|
||||
length_scale=params.length_scale,
|
||||
temperature=params.temperature,
|
||||
device=device,
|
||||
)
|
||||
output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
|
||||
|
||||
sf.write(
|
||||
file=params.save_wav_dir / f"{cut_ids[i]}_pred.wav",
|
||||
data=output["waveform"],
|
||||
samplerate=params.data_args.sampling_rate,
|
||||
subtype="PCM_16",
|
||||
)
|
||||
sf.write(
|
||||
file=params.save_wav_dir / f"{cut_ids[i]}_gt.wav",
|
||||
data=audio[i].numpy(),
|
||||
samplerate=params.data_args.sampling_rate,
|
||||
subtype="PCM_16",
|
||||
)
|
||||
|
||||
num_cuts += batch_size
|
||||
|
||||
if batch_idx % log_interval == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
BakerZhTtsDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
params.suffix = f"epoch-{params.epoch}"
|
||||
|
||||
params.res_dir = params.exp_dir / "infer" / params.suffix
|
||||
params.save_wav_dir = params.res_dir / "wav"
|
||||
params.save_wav_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
|
||||
logging.info("Infer started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
tokenizer = Tokenizer(params.tokens)
|
||||
params.vocab_size = tokenizer.vocab_size
|
||||
params.model_args.n_vocab = params.vocab_size
|
||||
|
||||
with open(params.cmvn) as f:
|
||||
stats = json.load(f)
|
||||
params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.data_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
|
||||
params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.model_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
|
||||
# Number of ODE Solver steps
|
||||
params.n_timesteps = 2
|
||||
|
||||
# Changes to the speaking rate
|
||||
params.length_scale = 1.0
|
||||
|
||||
# Sampling temperature
|
||||
params.temperature = 0.667
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_model(params)
|
||||
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
# we need cut ids to organize tts results.
|
||||
args.return_cuts = True
|
||||
baker_zh = BakerZhTtsDataModule(args)
|
||||
|
||||
test_cuts = baker_zh.test_cuts()
|
||||
test_dl = baker_zh.test_dataloaders(test_cuts)
|
||||
|
||||
if not Path(params.vocoder).is_file():
|
||||
raise ValueError(f"{params.vocoder} does not exist")
|
||||
|
||||
vocoder = load_vocoder(params.vocoder)
|
||||
vocoder.to(device)
|
||||
|
||||
denoiser = Denoiser(vocoder, mode="zeros")
|
||||
denoiser.to(device)
|
||||
|
||||
if params.input_text is not None and params.output_wav is not None:
|
||||
logging.info("Synthesizing a single text")
|
||||
output = synthesize(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
n_timesteps=params.n_timesteps,
|
||||
text=params.input_text,
|
||||
length_scale=params.length_scale,
|
||||
temperature=params.temperature,
|
||||
device=device,
|
||||
)
|
||||
output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
|
||||
|
||||
sf.write(
|
||||
file=params.output_wav,
|
||||
data=output["waveform"],
|
||||
samplerate=params.sampling_rate,
|
||||
subtype="PCM_16",
|
||||
)
|
||||
else:
|
||||
logging.info("Decoding the test set")
|
||||
infer_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
vocoder=vocoder,
|
||||
denoiser=denoiser,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
egs/baker_zh/TTS/matcha/model.py
Symbolic link
1
egs/baker_zh/TTS/matcha/model.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/model.py
|
||||
1
egs/baker_zh/TTS/matcha/models
Symbolic link
1
egs/baker_zh/TTS/matcha/models
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/models
|
||||
1
egs/baker_zh/TTS/matcha/monotonic_align
Symbolic link
1
egs/baker_zh/TTS/matcha/monotonic_align
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/monotonic_align
|
||||
316
egs/baker_zh/TTS/matcha/onnx_pretrained.py
Executable file
316
egs/baker_zh/TTS/matcha/onnx_pretrained.py
Executable file
@ -0,0 +1,316 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
|
||||
"""
|
||||
python3 ./matcha/onnx_pretrained.py \
|
||||
--acoustic-model ./model-steps-4.onnx \
|
||||
--vocoder ./hifigan_v2.onnx \
|
||||
--tokens ./data/tokens.txt \
|
||||
--lexicon ./lexicon.txt \
|
||||
--input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
|
||||
--output-wav ./b.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import datetime as dt
|
||||
import logging
|
||||
import re
|
||||
from typing import Dict, List
|
||||
|
||||
import jieba
|
||||
import onnxruntime as ort
|
||||
import soundfile as sf
|
||||
import torch
|
||||
from infer import load_vocoder
|
||||
from utils import intersperse
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--acoustic-model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the acoustic model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tokens",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lexicon",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the lexicon.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--vocoder",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the vocoder",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--input-text",
|
||||
type=str,
|
||||
required=True,
|
||||
help="The text to generate speech for",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--output-wav",
|
||||
type=str,
|
||||
required=True,
|
||||
help="The filename of the wave to save the generated speech",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
class OnnxHifiGANModel:
|
||||
def __init__(
|
||||
self,
|
||||
filename: str,
|
||||
):
|
||||
session_opts = ort.SessionOptions()
|
||||
session_opts.inter_op_num_threads = 1
|
||||
session_opts.intra_op_num_threads = 1
|
||||
|
||||
self.session_opts = session_opts
|
||||
self.model = ort.InferenceSession(
|
||||
filename,
|
||||
sess_options=self.session_opts,
|
||||
providers=["CPUExecutionProvider"],
|
||||
)
|
||||
|
||||
for i in self.model.get_inputs():
|
||||
print(i)
|
||||
|
||||
print("-----")
|
||||
|
||||
for i in self.model.get_outputs():
|
||||
print(i)
|
||||
|
||||
def __call__(self, x: torch.tensor):
|
||||
assert x.ndim == 3, x.shape
|
||||
assert x.shape[0] == 1, x.shape
|
||||
|
||||
audio = self.model.run(
|
||||
[self.model.get_outputs()[0].name],
|
||||
{
|
||||
self.model.get_inputs()[0].name: x.numpy(),
|
||||
},
|
||||
)[0]
|
||||
# audio: (batch_size, num_samples)
|
||||
|
||||
return torch.from_numpy(audio)
|
||||
|
||||
|
||||
class OnnxModel:
|
||||
def __init__(
|
||||
self,
|
||||
filename: str,
|
||||
):
|
||||
session_opts = ort.SessionOptions()
|
||||
session_opts.inter_op_num_threads = 1
|
||||
session_opts.intra_op_num_threads = 2
|
||||
|
||||
self.session_opts = session_opts
|
||||
self.model = ort.InferenceSession(
|
||||
filename,
|
||||
sess_options=self.session_opts,
|
||||
providers=["CPUExecutionProvider"],
|
||||
)
|
||||
|
||||
logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
|
||||
metadata = self.model.get_modelmeta().custom_metadata_map
|
||||
self.sample_rate = int(metadata["sample_rate"])
|
||||
|
||||
for i in self.model.get_inputs():
|
||||
print(i)
|
||||
|
||||
print("-----")
|
||||
|
||||
for i in self.model.get_outputs():
|
||||
print(i)
|
||||
|
||||
def __call__(self, x: torch.tensor):
|
||||
assert x.ndim == 2, x.shape
|
||||
assert x.shape[0] == 1, x.shape
|
||||
|
||||
x_lengths = torch.tensor([x.shape[1]], dtype=torch.int64)
|
||||
print("x_lengths", x_lengths)
|
||||
print("x", x.shape)
|
||||
|
||||
noise_scale = torch.tensor([1.0], dtype=torch.float32)
|
||||
length_scale = torch.tensor([1.0], dtype=torch.float32)
|
||||
|
||||
mel = self.model.run(
|
||||
[self.model.get_outputs()[0].name],
|
||||
{
|
||||
self.model.get_inputs()[0].name: x.numpy(),
|
||||
self.model.get_inputs()[1].name: x_lengths.numpy(),
|
||||
self.model.get_inputs()[2].name: noise_scale.numpy(),
|
||||
self.model.get_inputs()[3].name: length_scale.numpy(),
|
||||
},
|
||||
)[0]
|
||||
# mel: (batch_size, feat_dim, num_frames)
|
||||
|
||||
return torch.from_numpy(mel)
|
||||
|
||||
|
||||
def read_tokens(filename: str) -> Dict[str, int]:
|
||||
token2id = dict()
|
||||
with open(filename, encoding="utf-8") as f:
|
||||
for line in f.readlines():
|
||||
info = line.rstrip().split()
|
||||
if len(info) == 1:
|
||||
# case of space
|
||||
token = " "
|
||||
idx = int(info[0])
|
||||
else:
|
||||
token, idx = info[0], int(info[1])
|
||||
assert token not in token2id, token
|
||||
token2id[token] = idx
|
||||
return token2id
|
||||
|
||||
|
||||
def read_lexicon(filename: str) -> Dict[str, List[str]]:
|
||||
word2token = dict()
|
||||
with open(filename, encoding="utf-8") as f:
|
||||
for line in f.readlines():
|
||||
info = line.rstrip().split()
|
||||
w = info[0]
|
||||
tokens = info[1:]
|
||||
word2token[w] = tokens
|
||||
return word2token
|
||||
|
||||
|
||||
def convert_word_to_tokens(word2tokens: Dict[str, List[str]], word: str) -> List[str]:
|
||||
if word in word2tokens:
|
||||
return word2tokens[word]
|
||||
|
||||
if len(word) == 1:
|
||||
return []
|
||||
|
||||
ans = []
|
||||
for w in word:
|
||||
t = convert_word_to_tokens(word2tokens, w)
|
||||
ans.extend(t)
|
||||
return ans
|
||||
|
||||
|
||||
def normalize_text(text):
|
||||
whiter_space_re = re.compile(r"\s+")
|
||||
|
||||
punctuations_re = [
|
||||
(re.compile(x[0], re.IGNORECASE), x[1])
|
||||
for x in [
|
||||
("，", ","),
|
||||
("。", "."),
|
||||
("！", "!"),
|
||||
("？", "?"),
|
||||
("“", '"'),
|
||||
("”", '"'),
|
||||
("‘", "'"),
|
||||
("’", "'"),
|
||||
("：", ":"),
|
||||
("、", ","),
|
||||
]
|
||||
]
|
||||
|
||||
for regex, replacement in punctuations_re:
|
||||
text = re.sub(regex, replacement, text)
|
||||
return text
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
params = get_parser().parse_args()
|
||||
logging.info(vars(params))
|
||||
token2id = read_tokens(params.tokens)
|
||||
word2tokens = read_lexicon(params.lexicon)
|
||||
|
||||
text = normalize_text(params.input_text)
|
||||
seg = jieba.cut(text)
|
||||
tokens = []
|
||||
for s in seg:
|
||||
if s in token2id:
|
||||
tokens.append(s)
|
||||
continue
|
||||
|
||||
t = convert_word_to_tokens(word2tokens, s)
|
||||
if t:
|
||||
tokens.extend(t)
|
||||
|
||||
model = OnnxModel(params.acoustic_model)
|
||||
vocoder = OnnxHifiGANModel(params.vocoder)
|
||||
|
||||
x = []
|
||||
for t in tokens:
|
||||
if t in token2id:
|
||||
x.append(token2id[t])
|
||||
|
||||
x = intersperse(x, item=token2id["_"])
|
||||
|
||||
x = torch.tensor(x, dtype=torch.int64).unsqueeze(0)
|
||||
|
||||
start_t = dt.datetime.now()
|
||||
mel = model(x)
|
||||
end_t = dt.datetime.now()
|
||||
|
||||
start_t2 = dt.datetime.now()
|
||||
audio = vocoder(mel)
|
||||
end_t2 = dt.datetime.now()
|
||||
|
||||
print("audio", audio.shape) # (1, 1, num_samples)
|
||||
audio = audio.squeeze()
|
||||
|
||||
sample_rate = model.sample_rate
|
||||
|
||||
t = (end_t - start_t).total_seconds()
|
||||
t2 = (end_t2 - start_t2).total_seconds()
|
||||
rtf_am = t * sample_rate / audio.shape[-1]
|
||||
rtf_vocoder = t2 * sample_rate / audio.shape[-1]
|
||||
print("RTF for acoustic model ", rtf_am)
|
||||
print("RTF for vocoder", rtf_vocoder)
|
||||
|
||||
# skip denoiser
|
||||
sf.write(params.output_wav, audio, sample_rate, "PCM_16")
|
||||
logging.info(f"Saved to {params.output_wav}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
|
||||
"""
|
||||
|
||||
|HifiGAN |RTF |#Parameters (M)|
|
||||
|----------|-----|---------------|
|
||||
|v1 |0.818| 13.926 |
|
||||
|v2 |0.101| 0.925 |
|
||||
|v3 |0.118| 1.462 |
|
||||
|
||||
|Num steps|Acoustic Model RTF|
|
||||
|---------|------------------|
|
||||
| 2 | 0.039 |
|
||||
| 3 | 0.047 |
|
||||
| 4 | 0.071 |
|
||||
| 5 | 0.076 |
|
||||
| 6 | 0.103 |
|
||||
|
||||
"""
|
||||
119
egs/baker_zh/TTS/matcha/tokenizer.py
Normal file
119
egs/baker_zh/TTS/matcha/tokenizer.py
Normal file
@ -0,0 +1,119 @@
|
||||
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
|
||||
import logging
|
||||
from typing import Dict, List
|
||||
|
||||
import tacotron_cleaner.cleaners
|
||||
|
||||
try:
|
||||
from piper_phonemize import phonemize_espeak
|
||||
except Exception as ex:
|
||||
raise RuntimeError(
|
||||
f"{ex}\nPlease run\n"
|
||||
"pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
|
||||
)
|
||||
|
||||
from utils import intersperse
|
||||
|
||||
|
||||
# This tokenizer supports both English and Chinese.
|
||||
# We assume you have used
|
||||
# ../local/convert_text_to_tokens.py
|
||||
# to process your text
|
||||
class Tokenizer(object):
|
||||
def __init__(self, tokens: str):
|
||||
"""
|
||||
Args:
|
||||
tokens: the file that maps tokens to ids
|
||||
"""
|
||||
# Parse token file
|
||||
self.token2id: Dict[str, int] = {}
|
||||
with open(tokens, "r", encoding="utf-8") as f:
|
||||
for line in f.readlines():
|
||||
info = line.rstrip().split()
|
||||
if len(info) == 1:
|
||||
# case of space
|
||||
token = " "
|
||||
id = int(info[0])
|
||||
else:
|
||||
token, id = info[0], int(info[1])
|
||||
assert token not in self.token2id, token
|
||||
self.token2id[token] = id
|
||||
|
||||
# Refer to https://github.com/rhasspy/piper/blob/master/TRAINING.md
|
||||
self.pad_id = self.token2id["_"] # padding
|
||||
self.space_id = self.token2id[" "] # word separator (whitespace)
|
||||
|
||||
self.vocab_size = len(self.token2id)
|
||||
|
||||
def texts_to_token_ids(
|
||||
self,
|
||||
sentence_list: List[List[str]],
|
||||
intersperse_blank: bool = True,
|
||||
lang: str = "en-us",
|
||||
) -> List[List[int]]:
|
||||
"""
|
||||
Args:
|
||||
sentence_list:
|
||||
A list of sentences.
|
||||
intersperse_blank:
|
||||
Whether to intersperse blanks in the token sequence.
|
||||
lang:
|
||||
Language argument passed to phonemize_espeak().
|
||||
|
||||
Returns:
|
||||
Return a list of token id list [utterance][token_id]
|
||||
"""
|
||||
token_ids_list = []
|
||||
|
||||
for sentence in sentence_list:
|
||||
tokens_list = []
|
||||
for word in sentence:
|
||||
if word in self.token2id:
|
||||
tokens_list.append(word)
|
||||
continue
|
||||
|
||||
tmp_tokens_list = phonemize_espeak(word, lang)
|
||||
for t in tmp_tokens_list:
|
||||
tokens_list.extend(t)
|
||||
|
||||
token_ids = []
|
||||
for t in tokens_list:
|
||||
if t not in self.token2id:
|
||||
logging.warning(f"Skip OOV {t} {sentence}")
|
||||
continue
|
||||
|
||||
if t == " " and len(token_ids) > 0 and token_ids[-1] == self.space_id:
|
||||
continue
|
||||
|
||||
token_ids.append(self.token2id[t])
|
||||
|
||||
if intersperse_blank:
|
||||
token_ids = intersperse(token_ids, self.pad_id)
|
||||
|
||||
token_ids_list.append(token_ids)
|
||||
|
||||
return token_ids_list
|
||||
|
||||
|
||||
def test_tokenizer():
|
||||
import jieba
|
||||
from pypinyin import Style, lazy_pinyin
|
||||
|
||||
tokenizer = Tokenizer("data/tokens.txt")
|
||||
text1 = "今天is Monday, tomorrow is 星期二"
|
||||
text2 = "你好吗? 我很好, how about you?"
|
||||
|
||||
text1 = list(jieba.cut(text1))
|
||||
text2 = list(jieba.cut(text2))
|
||||
tokens1 = lazy_pinyin(text1, style=Style.TONE3, tone_sandhi=True)
|
||||
tokens2 = lazy_pinyin(text2, style=Style.TONE3, tone_sandhi=True)
|
||||
print(tokens1)
|
||||
print(tokens2)
|
||||
|
||||
ids = tokenizer.texts_to_token_ids([tokens1, tokens2])
|
||||
print(ids)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_tokenizer()
|
||||
717
egs/baker_zh/TTS/matcha/train.py
Executable file
717
egs/baker_zh/TTS/matcha/train.py
Executable file
@ -0,0 +1,717 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
from typing import Any, Dict, Optional, Union
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
from lhotse.utils import fix_random_seed
|
||||
from model import fix_len_compatibility
|
||||
from models.matcha_tts import MatchaTTS
|
||||
from tokenizer import Tokenizer
|
||||
from torch.cuda.amp import GradScaler, autocast
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.optim import Optimizer
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
from tts_datamodule import BakerZhTtsDataModule
|
||||
from utils import MetricsTracker
|
||||
|
||||
from icefall.checkpoint import load_checkpoint, save_checkpoint
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.utils import AttributeDict, setup_logger, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--world-size",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of GPUs for DDP training.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--master-port",
|
||||
type=int,
|
||||
default=12335,
|
||||
help="Master port to use for DDP training.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tensorboard",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Should various information be logged in tensorboard.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-epochs",
|
||||
type=int,
|
||||
default=1000,
|
||||
help="Number of epochs to train.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--start-epoch",
|
||||
type=int,
|
||||
default=1,
|
||||
help="""Resume training from this epoch. It should be positive.
|
||||
If larger than 1, it will load checkpoint from
|
||||
exp-dir/epoch-{start_epoch-1}.pt
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=Path,
|
||||
default="matcha/exp",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/tokens.txt",
|
||||
help="""Path to vocabulary.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--cmvn",
|
||||
type=str,
|
||||
default="data/fbank/cmvn.json",
|
||||
help="""Path to vocabulary.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=42,
|
||||
help="The seed for random generators intended for reproducibility",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--save-every-n",
|
||||
type=int,
|
||||
default=10,
|
||||
help="""Save checkpoint after processing this number of epochs"
|
||||
periodically. We save checkpoint to exp-dir/ whenever
|
||||
params.cur_epoch % save_every_n == 0. The checkpoint filename
|
||||
has the form: f'exp-dir/epoch-{params.cur_epoch}.pt'.
|
||||
Since it will take around 1000 epochs, we suggest using a large
|
||||
save_every_n to save disk space.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-fp16",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Whether to use half precision training.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def get_data_statistics():
|
||||
return AttributeDict(
|
||||
{
|
||||
"mel_mean": 0,
|
||||
"mel_std": 1,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _get_data_params() -> AttributeDict:
|
||||
params = AttributeDict(
|
||||
{
|
||||
"name": "baker-zh",
|
||||
"train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt",
|
||||
"valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt",
|
||||
# "batch_size": 64,
|
||||
# "num_workers": 1,
|
||||
# "pin_memory": False,
|
||||
"cleaners": ["english_cleaners2"],
|
||||
"add_blank": True,
|
||||
"n_spks": 1,
|
||||
"n_fft": 1024,
|
||||
"n_feats": 80,
|
||||
"sampling_rate": 22050,
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
|
||||
"f_min": 0,
|
||||
"f_max": 8000,
|
||||
"seed": 1234,
|
||||
"load_durations": False,
|
||||
"data_statistics": get_data_statistics(),
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def _get_model_params() -> AttributeDict:
|
||||
n_feats = 80
|
||||
filter_channels_dp = 256
|
||||
encoder_params_p_dropout = 0.1
|
||||
params = AttributeDict(
|
||||
{
|
||||
"n_spks": 1, # for baker-zh.
|
||||
"spk_emb_dim": 64,
|
||||
"n_feats": n_feats,
|
||||
"out_size": None, # or use 172
|
||||
"prior_loss": True,
|
||||
"use_precomputed_durations": False,
|
||||
"data_statistics": get_data_statistics(),
|
||||
"encoder": AttributeDict(
|
||||
{
|
||||
"encoder_type": "RoPE Encoder", # not used
|
||||
"encoder_params": AttributeDict(
|
||||
{
|
||||
"n_feats": n_feats,
|
||||
"n_channels": 192,
|
||||
"filter_channels": 768,
|
||||
"filter_channels_dp": filter_channels_dp,
|
||||
"n_heads": 2,
|
||||
"n_layers": 6,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": encoder_params_p_dropout,
|
||||
"spk_emb_dim": 64,
|
||||
"n_spks": 1,
|
||||
"prenet": True,
|
||||
}
|
||||
),
|
||||
"duration_predictor_params": AttributeDict(
|
||||
{
|
||||
"filter_channels_dp": filter_channels_dp,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": encoder_params_p_dropout,
|
||||
}
|
||||
),
|
||||
}
|
||||
),
|
||||
"decoder": AttributeDict(
|
||||
{
|
||||
"channels": [256, 256],
|
||||
"dropout": 0.05,
|
||||
"attention_head_dim": 64,
|
||||
"n_blocks": 1,
|
||||
"num_mid_blocks": 2,
|
||||
"num_heads": 2,
|
||||
"act_fn": "snakebeta",
|
||||
}
|
||||
),
|
||||
"cfm": AttributeDict(
|
||||
{
|
||||
"name": "CFM",
|
||||
"solver": "euler",
|
||||
"sigma_min": 1e-4,
|
||||
}
|
||||
),
|
||||
"optimizer": AttributeDict(
|
||||
{
|
||||
"lr": 1e-4,
|
||||
"weight_decay": 0.0,
|
||||
}
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
return params
|
||||
|
||||
|
||||
def get_params():
|
||||
params = AttributeDict(
|
||||
{
|
||||
"model_args": _get_model_params(),
|
||||
"data_args": _get_data_params(),
|
||||
"best_train_loss": float("inf"),
|
||||
"best_valid_loss": float("inf"),
|
||||
"best_train_epoch": -1,
|
||||
"best_valid_epoch": -1,
|
||||
"batch_idx_train": -1, # 0
|
||||
"log_interval": 10,
|
||||
"valid_interval": 1500,
|
||||
"env_info": get_env_info(),
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def get_model(params):
|
||||
m = MatchaTTS(**params.model_args)
|
||||
return m
|
||||
|
||||
|
||||
def load_checkpoint_if_available(
|
||||
params: AttributeDict, model: nn.Module
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""Load checkpoint from file.
|
||||
|
||||
If params.start_epoch is larger than 1, it will load the checkpoint from
|
||||
`params.start_epoch - 1`.
|
||||
|
||||
Apart from loading state dict for `model` and `optimizer` it also updates
|
||||
`best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
|
||||
and `best_valid_loss` in `params`.
|
||||
|
||||
Args:
|
||||
params:
|
||||
The return value of :func:`get_params`.
|
||||
model:
|
||||
The training model.
|
||||
Returns:
|
||||
Return a dict containing previously saved training info.
|
||||
"""
|
||||
if params.start_epoch > 1:
|
||||
filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
|
||||
else:
|
||||
return None
|
||||
|
||||
assert filename.is_file(), f"{filename} does not exist!"
|
||||
|
||||
saved_params = load_checkpoint(filename, model=model)
|
||||
|
||||
keys = [
|
||||
"best_train_epoch",
|
||||
"best_valid_epoch",
|
||||
"batch_idx_train",
|
||||
"best_train_loss",
|
||||
"best_valid_loss",
|
||||
]
|
||||
for k in keys:
|
||||
params[k] = saved_params[k]
|
||||
|
||||
return saved_params
|
||||
|
||||
|
||||
def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device, params):
|
||||
"""Parse batch data"""
|
||||
mel_mean = params.data_args.data_statistics.mel_mean
|
||||
mel_std_inv = 1 / params.data_args.data_statistics.mel_std
|
||||
for i in range(batch["features"].shape[0]):
|
||||
n = batch["features_lens"][i]
|
||||
batch["features"][i : i + 1, :n, :] = (
|
||||
batch["features"][i : i + 1, :n, :] - mel_mean
|
||||
) * mel_std_inv
|
||||
batch["features"][i : i + 1, n:, :] = 0
|
||||
|
||||
audio = batch["audio"].to(device)
|
||||
features = batch["features"].to(device)
|
||||
audio_lens = batch["audio_lens"].to(device)
|
||||
features_lens = batch["features_lens"].to(device)
|
||||
tokens = batch["tokens"]
|
||||
|
||||
tokens = tokenizer.texts_to_token_ids(tokens, intersperse_blank=True)
|
||||
tokens = k2.RaggedTensor(tokens)
|
||||
row_splits = tokens.shape.row_splits(1)
|
||||
tokens_lens = row_splits[1:] - row_splits[:-1]
|
||||
tokens = tokens.to(device)
|
||||
tokens_lens = tokens_lens.to(device)
|
||||
# a tensor of shape (B, T)
|
||||
tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id)
|
||||
|
||||
max_feature_length = fix_len_compatibility(features.shape[1])
|
||||
if max_feature_length > features.shape[1]:
|
||||
pad = max_feature_length - features.shape[1]
|
||||
features = torch.nn.functional.pad(features, (0, 0, 0, pad))
|
||||
|
||||
# features_lens[features_lens.argmax()] += pad
|
||||
|
||||
return audio, audio_lens, features, features_lens.long(), tokens, tokens_lens.long()
|
||||
|
||||
|
||||
def compute_validation_loss(
|
||||
params: AttributeDict,
|
||||
model: Union[nn.Module, DDP],
|
||||
tokenizer: Tokenizer,
|
||||
valid_dl: torch.utils.data.DataLoader,
|
||||
world_size: int = 1,
|
||||
rank: int = 0,
|
||||
) -> MetricsTracker:
|
||||
"""Run the validation process."""
|
||||
model.eval()
|
||||
device = model.device if isinstance(model, DDP) else next(model.parameters()).device
|
||||
get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
|
||||
|
||||
# used to summary the stats over iterations
|
||||
tot_loss = MetricsTracker()
|
||||
|
||||
with torch.no_grad():
|
||||
for batch_idx, batch in enumerate(valid_dl):
|
||||
(
|
||||
audio,
|
||||
audio_lens,
|
||||
features,
|
||||
features_lens,
|
||||
tokens,
|
||||
tokens_lens,
|
||||
) = prepare_input(batch, tokenizer, device, params)
|
||||
|
||||
losses = get_losses(
|
||||
{
|
||||
"x": tokens,
|
||||
"x_lengths": tokens_lens,
|
||||
"y": features.permute(0, 2, 1),
|
||||
"y_lengths": features_lens,
|
||||
"spks": None, # should change it for multi-speakers
|
||||
"durations": None,
|
||||
}
|
||||
)
|
||||
|
||||
batch_size = len(batch["tokens"])
|
||||
|
||||
loss_info = MetricsTracker()
|
||||
loss_info["samples"] = batch_size
|
||||
|
||||
s = 0
|
||||
|
||||
for key, value in losses.items():
|
||||
v = value.detach().item()
|
||||
loss_info[key] = v * batch_size
|
||||
s += v * batch_size
|
||||
|
||||
loss_info["tot_loss"] = s
|
||||
|
||||
# summary stats
|
||||
tot_loss = tot_loss + loss_info
|
||||
|
||||
if world_size > 1:
|
||||
tot_loss.reduce(device)
|
||||
|
||||
loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
|
||||
if loss_value < params.best_valid_loss:
|
||||
params.best_valid_epoch = params.cur_epoch
|
||||
params.best_valid_loss = loss_value
|
||||
|
||||
return tot_loss
|
||||
|
||||
|
||||
def train_one_epoch(
|
||||
params: AttributeDict,
|
||||
model: Union[nn.Module, DDP],
|
||||
tokenizer: Tokenizer,
|
||||
optimizer: Optimizer,
|
||||
train_dl: torch.utils.data.DataLoader,
|
||||
valid_dl: torch.utils.data.DataLoader,
|
||||
scaler: GradScaler,
|
||||
tb_writer: Optional[SummaryWriter] = None,
|
||||
world_size: int = 1,
|
||||
rank: int = 0,
|
||||
) -> None:
|
||||
"""Train the model for one epoch.
|
||||
|
||||
The training loss from the mean of all frames is saved in
|
||||
`params.train_loss`. It runs the validation process every
|
||||
`params.valid_interval` batches.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The model for training.
|
||||
optimizer:
|
||||
The optimizer.
|
||||
train_dl:
|
||||
Dataloader for the training dataset.
|
||||
valid_dl:
|
||||
Dataloader for the validation dataset.
|
||||
scaler:
|
||||
The scaler used for mix precision training.
|
||||
tb_writer:
|
||||
Writer to write log messages to tensorboard.
|
||||
"""
|
||||
model.train()
|
||||
device = model.device if isinstance(model, DDP) else next(model.parameters()).device
|
||||
get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
|
||||
|
||||
# used to track the stats over iterations in one epoch
|
||||
tot_loss = MetricsTracker()
|
||||
|
||||
saved_bad_model = False
|
||||
|
||||
def save_bad_model(suffix: str = ""):
|
||||
save_checkpoint(
|
||||
filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
|
||||
model=model,
|
||||
params=params,
|
||||
optimizer=optimizer,
|
||||
scaler=scaler,
|
||||
rank=0,
|
||||
)
|
||||
|
||||
for batch_idx, batch in enumerate(train_dl):
|
||||
params.batch_idx_train += 1
|
||||
# audio: (N, T), float32
|
||||
# features: (N, T, C), float32
|
||||
# audio_lens, (N,), int32
|
||||
# features_lens, (N,), int32
|
||||
# tokens: List[List[str]], len(tokens) == N
|
||||
|
||||
batch_size = len(batch["tokens"])
|
||||
|
||||
(
|
||||
audio,
|
||||
audio_lens,
|
||||
features,
|
||||
features_lens,
|
||||
tokens,
|
||||
tokens_lens,
|
||||
) = prepare_input(batch, tokenizer, device, params)
|
||||
try:
|
||||
with autocast(enabled=params.use_fp16):
|
||||
losses = get_losses(
|
||||
{
|
||||
"x": tokens,
|
||||
"x_lengths": tokens_lens,
|
||||
"y": features.permute(0, 2, 1),
|
||||
"y_lengths": features_lens,
|
||||
"spks": None, # should change it for multi-speakers
|
||||
"durations": None,
|
||||
}
|
||||
)
|
||||
|
||||
loss = sum(losses.values())
|
||||
|
||||
scaler.scale(loss).backward()
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
|
||||
loss_info = MetricsTracker()
|
||||
loss_info["samples"] = batch_size
|
||||
|
||||
s = 0
|
||||
|
||||
for key, value in losses.items():
|
||||
v = value.detach().item()
|
||||
loss_info[key] = v * batch_size
|
||||
s += v * batch_size
|
||||
|
||||
loss_info["tot_loss"] = s
|
||||
|
||||
tot_loss = tot_loss + loss_info
|
||||
except: # noqa
|
||||
save_bad_model()
|
||||
raise
|
||||
|
||||
if params.batch_idx_train % 100 == 0 and params.use_fp16:
|
||||
# If the grad scale was less than 1, try increasing it.
|
||||
# The _growth_interval of the grad scaler is configurable,
|
||||
# but we can't configure it to have different
|
||||
# behavior depending on the current grad scale.
|
||||
cur_grad_scale = scaler._scale.item()
|
||||
|
||||
if cur_grad_scale < 8.0 or (
|
||||
cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0
|
||||
):
|
||||
scaler.update(cur_grad_scale * 2.0)
|
||||
if cur_grad_scale < 0.01:
|
||||
if not saved_bad_model:
|
||||
save_bad_model(suffix="-first-warning")
|
||||
saved_bad_model = True
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
save_bad_model()
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
|
||||
if params.batch_idx_train % params.log_interval == 0:
|
||||
cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
|
||||
|
||||
logging.info(
|
||||
f"Epoch {params.cur_epoch}, batch {batch_idx}, "
|
||||
f"global_batch_idx: {params.batch_idx_train}, "
|
||||
f"batch size: {batch_size}, "
|
||||
f"loss[{loss_info}], tot_loss[{tot_loss}], "
|
||||
+ (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
|
||||
)
|
||||
|
||||
if tb_writer is not None:
|
||||
loss_info.write_summary(
|
||||
tb_writer, "train/current_", params.batch_idx_train
|
||||
)
|
||||
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
|
||||
if params.use_fp16:
|
||||
tb_writer.add_scalar(
|
||||
"train/grad_scale", cur_grad_scale, params.batch_idx_train
|
||||
)
|
||||
|
||||
if params.batch_idx_train % params.valid_interval == 1:
|
||||
logging.info("Computing validation loss")
|
||||
valid_info = compute_validation_loss(
|
||||
params=params,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
valid_dl=valid_dl,
|
||||
world_size=world_size,
|
||||
rank=rank,
|
||||
)
|
||||
model.train()
|
||||
logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
|
||||
logging.info(
|
||||
"Maximum memory allocated so far is "
|
||||
f"{torch.cuda.max_memory_allocated()//1000000}MB"
|
||||
)
|
||||
if tb_writer is not None:
|
||||
valid_info.write_summary(
|
||||
tb_writer, "train/valid_", params.batch_idx_train
|
||||
)
|
||||
|
||||
loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
|
||||
params.train_loss = loss_value
|
||||
if params.train_loss < params.best_train_loss:
|
||||
params.best_train_epoch = params.cur_epoch
|
||||
params.best_train_loss = params.train_loss
|
||||
|
||||
|
||||
def run(rank, world_size, args):
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
fix_random_seed(params.seed)
|
||||
if world_size > 1:
|
||||
setup_dist(rank, world_size, params.master_port)
|
||||
|
||||
setup_logger(f"{params.exp_dir}/log/log-train")
|
||||
logging.info("Training started")
|
||||
|
||||
if args.tensorboard and rank == 0:
|
||||
tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
|
||||
else:
|
||||
tb_writer = None
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", rank)
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
tokenizer = Tokenizer(params.tokens)
|
||||
params.pad_id = tokenizer.pad_id
|
||||
params.vocab_size = tokenizer.vocab_size
|
||||
params.model_args.n_vocab = params.vocab_size
|
||||
|
||||
with open(params.cmvn) as f:
|
||||
stats = json.load(f)
|
||||
params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.data_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
|
||||
params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
|
||||
params.model_args.data_statistics.mel_std = stats["fbank_std"]
|
||||
|
||||
logging.info(params)
|
||||
print(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of parameters: {num_param}")
|
||||
|
||||
assert params.start_epoch > 0, params.start_epoch
|
||||
checkpoints = load_checkpoint_if_available(params=params, model=model)
|
||||
|
||||
model.to(device)
|
||||
|
||||
if world_size > 1:
|
||||
logging.info("Using DDP")
|
||||
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
|
||||
|
||||
optimizer = torch.optim.Adam(model.parameters(), **params.model_args.optimizer)
|
||||
|
||||
logging.info("About to create datamodule")
|
||||
|
||||
baker_zh = BakerZhTtsDataModule(args)
|
||||
|
||||
train_cuts = baker_zh.train_cuts()
|
||||
train_dl = baker_zh.train_dataloaders(train_cuts)
|
||||
|
||||
valid_cuts = baker_zh.valid_cuts()
|
||||
valid_dl = baker_zh.valid_dataloaders(valid_cuts)
|
||||
|
||||
scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
|
||||
if checkpoints and "grad_scaler" in checkpoints:
|
||||
logging.info("Loading grad scaler state dict")
|
||||
scaler.load_state_dict(checkpoints["grad_scaler"])
|
||||
|
||||
for epoch in range(params.start_epoch, params.num_epochs + 1):
|
||||
logging.info(f"Start epoch {epoch}")
|
||||
fix_random_seed(params.seed + epoch - 1)
|
||||
if "sampler" in train_dl:
|
||||
train_dl.sampler.set_epoch(epoch - 1)
|
||||
|
||||
params.cur_epoch = epoch
|
||||
|
||||
if tb_writer is not None:
|
||||
tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
|
||||
|
||||
train_one_epoch(
|
||||
params=params,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
optimizer=optimizer,
|
||||
train_dl=train_dl,
|
||||
valid_dl=valid_dl,
|
||||
scaler=scaler,
|
||||
tb_writer=tb_writer,
|
||||
world_size=world_size,
|
||||
rank=rank,
|
||||
)
|
||||
|
||||
if epoch % params.save_every_n == 0 or epoch == params.num_epochs:
|
||||
filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
|
||||
save_checkpoint(
|
||||
filename=filename,
|
||||
params=params,
|
||||
model=model,
|
||||
optimizer=optimizer,
|
||||
scaler=scaler,
|
||||
rank=rank,
|
||||
)
|
||||
if rank == 0:
|
||||
if params.best_train_epoch == params.cur_epoch:
|
||||
best_train_filename = params.exp_dir / "best-train-loss.pt"
|
||||
copyfile(src=filename, dst=best_train_filename)
|
||||
|
||||
if params.best_valid_epoch == params.cur_epoch:
|
||||
best_valid_filename = params.exp_dir / "best-valid-loss.pt"
|
||||
copyfile(src=filename, dst=best_valid_filename)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
if world_size > 1:
|
||||
torch.distributed.barrier()
|
||||
cleanup_dist()
|
||||
|
||||
|
||||
def main():
|
||||
parser = get_parser()
|
||||
BakerZhTtsDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
|
||||
world_size = args.world_size
|
||||
assert world_size >= 1
|
||||
if world_size > 1:
|
||||
mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
|
||||
else:
|
||||
run(rank=0, world_size=1, args=args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
main()
|
||||
340
egs/baker_zh/TTS/matcha/tts_datamodule.py
Normal file
340
egs/baker_zh/TTS/matcha/tts_datamodule.py
Normal file
@ -0,0 +1,340 @@
|
||||
# Copyright 2021 Piotr Żelasko
|
||||
# Copyright 2022-2023 Xiaomi Corporation (Authors: Mingshuang Luo,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import torch
|
||||
from fbank import MatchaFbank, MatchaFbankConfig
|
||||
from lhotse import CutSet, load_manifest_lazy
|
||||
from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures
|
||||
CutConcatenate,
|
||||
CutMix,
|
||||
DynamicBucketingSampler,
|
||||
PrecomputedFeatures,
|
||||
SimpleCutSampler,
|
||||
SpeechSynthesisDataset,
|
||||
)
|
||||
from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples
|
||||
AudioSamples,
|
||||
OnTheFlyFeatures,
|
||||
)
|
||||
from lhotse.utils import fix_random_seed
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from icefall.utils import str2bool
|
||||
|
||||
|
||||
class _SeedWorkers:
|
||||
def __init__(self, seed: int):
|
||||
self.seed = seed
|
||||
|
||||
def __call__(self, worker_id: int):
|
||||
fix_random_seed(self.seed + worker_id)
|
||||
|
||||
|
||||
class BakerZhTtsDataModule:
|
||||
"""
|
||||
DataModule for tts experiments.
|
||||
It assumes there is always one train and valid dataloader,
|
||||
but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
|
||||
and test-other).
|
||||
|
||||
It contains all the common data pipeline modules used in ASR
|
||||
experiments, e.g.:
|
||||
- dynamic batch size,
|
||||
- bucketing samplers,
|
||||
- cut concatenation,
|
||||
- on-the-fly feature extraction
|
||||
|
||||
This class should be derived for specific corpora used in ASR tasks.
|
||||
"""
|
||||
|
||||
def __init__(self, args: argparse.Namespace):
|
||||
self.args = args
|
||||
|
||||
@classmethod
|
||||
def add_arguments(cls, parser: argparse.ArgumentParser):
|
||||
group = parser.add_argument_group(
|
||||
title="TTS data related options",
|
||||
description="These options are used for the preparation of "
|
||||
"PyTorch DataLoaders from Lhotse CutSet's -- they control the "
|
||||
"effective batch sizes, sampling strategies, applied data "
|
||||
"augmentations, etc.",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--manifest-dir",
|
||||
type=Path,
|
||||
default=Path("data/fbank"),
|
||||
help="Path to directory with train/valid/test cuts.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--max-duration",
|
||||
type=int,
|
||||
default=200.0,
|
||||
help="Maximum pooled recordings duration (seconds) in a "
|
||||
"single batch. You can reduce it if it causes CUDA OOM.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--bucketing-sampler",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="When enabled, the batches will come from buckets of "
|
||||
"similar duration (saves padding frames).",
|
||||
)
|
||||
group.add_argument(
|
||||
"--num-buckets",
|
||||
type=int,
|
||||
default=30,
|
||||
help="The number of buckets for the DynamicBucketingSampler"
|
||||
"(you might want to increase it for larger datasets).",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--on-the-fly-feats",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="When enabled, use on-the-fly cut mixing and feature "
|
||||
"extraction. Will drop existing precomputed feature manifests "
|
||||
"if available.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--shuffle",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="When enabled (=default), the examples will be "
|
||||
"shuffled for each epoch.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--drop-last",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Whether to drop last batch. Used by sampler.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--return-cuts",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="When enabled, each batch will have the "
|
||||
"field: batch['cut'] with the cuts that "
|
||||
"were used to construct it.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--num-workers",
|
||||
type=int,
|
||||
default=2,
|
||||
help="The number of training dataloader workers that "
|
||||
"collect the batches.",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--input-strategy",
|
||||
type=str,
|
||||
default="PrecomputedFeatures",
|
||||
help="AudioSamples or PrecomputedFeatures",
|
||||
)
|
||||
|
||||
def train_dataloaders(
|
||||
self,
|
||||
cuts_train: CutSet,
|
||||
sampler_state_dict: Optional[Dict[str, Any]] = None,
|
||||
) -> DataLoader:
|
||||
"""
|
||||
Args:
|
||||
cuts_train:
|
||||
CutSet for training.
|
||||
sampler_state_dict:
|
||||
The state dict for the training sampler.
|
||||
"""
|
||||
logging.info("About to create train dataset")
|
||||
train = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=eval(self.args.input_strategy)(),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
|
||||
if self.args.on_the_fly_feats:
|
||||
sampling_rate = 22050
|
||||
config = MatchaFbankConfig(
|
||||
n_fft=1024,
|
||||
n_mels=80,
|
||||
sampling_rate=sampling_rate,
|
||||
hop_length=256,
|
||||
win_length=1024,
|
||||
f_min=0,
|
||||
f_max=8000,
|
||||
)
|
||||
train = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
|
||||
if self.args.bucketing_sampler:
|
||||
logging.info("Using DynamicBucketingSampler.")
|
||||
train_sampler = DynamicBucketingSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=self.args.drop_last,
|
||||
)
|
||||
else:
|
||||
logging.info("Using SimpleCutSampler.")
|
||||
train_sampler = SimpleCutSampler(
|
||||
cuts_train,
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
)
|
||||
logging.info("About to create train dataloader")
|
||||
|
||||
if sampler_state_dict is not None:
|
||||
logging.info("Loading sampler state dict")
|
||||
train_sampler.load_state_dict(sampler_state_dict)
|
||||
|
||||
# 'seed' is derived from the current random state, which will have
|
||||
# previously been set in the main process.
|
||||
seed = torch.randint(0, 100000, ()).item()
|
||||
worker_init_fn = _SeedWorkers(seed)
|
||||
|
||||
train_dl = DataLoader(
|
||||
train,
|
||||
sampler=train_sampler,
|
||||
batch_size=None,
|
||||
num_workers=self.args.num_workers,
|
||||
persistent_workers=True,
|
||||
pin_memory=True,
|
||||
worker_init_fn=worker_init_fn,
|
||||
)
|
||||
|
||||
return train_dl
|
||||
|
||||
def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
|
||||
logging.info("About to create dev dataset")
|
||||
if self.args.on_the_fly_feats:
|
||||
sampling_rate = 22050
|
||||
config = MatchaFbankConfig(
|
||||
n_fft=1024,
|
||||
n_mels=80,
|
||||
sampling_rate=sampling_rate,
|
||||
hop_length=256,
|
||||
win_length=1024,
|
||||
f_min=0,
|
||||
f_max=8000,
|
||||
)
|
||||
validate = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
else:
|
||||
validate = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=eval(self.args.input_strategy)(),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
valid_sampler = DynamicBucketingSampler(
|
||||
cuts_valid,
|
||||
max_duration=self.args.max_duration,
|
||||
num_buckets=self.args.num_buckets,
|
||||
shuffle=False,
|
||||
)
|
||||
logging.info("About to create valid dataloader")
|
||||
valid_dl = DataLoader(
|
||||
validate,
|
||||
sampler=valid_sampler,
|
||||
batch_size=None,
|
||||
num_workers=2,
|
||||
persistent_workers=True,
|
||||
pin_memory=True,
|
||||
)
|
||||
|
||||
return valid_dl
|
||||
|
||||
def test_dataloaders(self, cuts: CutSet) -> DataLoader:
|
||||
logging.info("About to create test dataset")
|
||||
if self.args.on_the_fly_feats:
|
||||
sampling_rate = 22050
|
||||
config = MatchaFbankConfig(
|
||||
n_fft=1024,
|
||||
n_mels=80,
|
||||
sampling_rate=sampling_rate,
|
||||
hop_length=256,
|
||||
win_length=1024,
|
||||
f_min=0,
|
||||
f_max=8000,
|
||||
)
|
||||
test = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
else:
|
||||
test = SpeechSynthesisDataset(
|
||||
return_text=False,
|
||||
return_tokens=True,
|
||||
feature_input_strategy=eval(self.args.input_strategy)(),
|
||||
return_cuts=self.args.return_cuts,
|
||||
)
|
||||
test_sampler = DynamicBucketingSampler(
|
||||
cuts,
|
||||
max_duration=self.args.max_duration,
|
||||
num_buckets=self.args.num_buckets,
|
||||
shuffle=False,
|
||||
)
|
||||
logging.info("About to create test dataloader")
|
||||
test_dl = DataLoader(
|
||||
test,
|
||||
batch_size=None,
|
||||
sampler=test_sampler,
|
||||
num_workers=self.args.num_workers,
|
||||
)
|
||||
return test_dl
|
||||
|
||||
@lru_cache()
|
||||
def train_cuts(self) -> CutSet:
|
||||
logging.info("About to get train cuts")
|
||||
return load_manifest_lazy(
|
||||
self.args.manifest_dir / "baker_zh_cuts_train.jsonl.gz"
|
||||
)
|
||||
|
||||
@lru_cache()
|
||||
def valid_cuts(self) -> CutSet:
|
||||
logging.info("About to get validation cuts")
|
||||
return load_manifest_lazy(
|
||||
self.args.manifest_dir / "baker_zh_cuts_valid.jsonl.gz"
|
||||
)
|
||||
|
||||
@lru_cache()
|
||||
def test_cuts(self) -> CutSet:
|
||||
logging.info("About to get test cuts")
|
||||
return load_manifest_lazy(
|
||||
self.args.manifest_dir / "baker_zh_cuts_test.jsonl.gz"
|
||||
)
|
||||
1
egs/baker_zh/TTS/matcha/utils.py
Symbolic link
1
egs/baker_zh/TTS/matcha/utils.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../ljspeech/TTS/matcha/utils.py
|
||||
151
egs/baker_zh/TTS/prepare.sh
Executable file
151
egs/baker_zh/TTS/prepare.sh
Executable file
@ -0,0 +1,151 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
|
||||
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
|
||||
set -eou pipefail
|
||||
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
|
||||
dl_dir=$PWD/download
|
||||
mkdir -p $dl_dir
|
||||
|
||||
. shared/parse_options.sh || exit 1
|
||||
|
||||
# All files generated by this script are saved in "data".
|
||||
# You can safely remove "data" and rerun this script to regenerate it.
|
||||
mkdir -p data
|
||||
|
||||
log() {
|
||||
# This function is from espnet
|
||||
local fname=${BASH_SOURCE[1]##*/}
|
||||
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
|
||||
}
|
||||
|
||||
log "dl_dir: $dl_dir"
|
||||
|
||||
if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
|
||||
log "Stage -1: build monotonic_align lib (used by ./matcha)"
|
||||
for recipe in matcha; do
|
||||
if [ ! -d $recipe/monotonic_align/build ]; then
|
||||
cd $recipe/monotonic_align
|
||||
python3 setup.py build_ext --inplace
|
||||
cd ../../
|
||||
else
|
||||
log "monotonic_align lib for $recipe already built"
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
|
||||
log "Stage 0: Download data"
|
||||
|
||||
# The directory $dl_dir/BANSYP contains the following 3 directories
|
||||
|
||||
# ls -lh $dl_dir/BZNSYP/
|
||||
# total 0
|
||||
# drwxr-xr-x 10002 kuangfangjun root 0 Jan 4 2019 PhoneLabeling
|
||||
# drwxr-xr-x 3 kuangfangjun root 0 Jan 31 2019 ProsodyLabeling
|
||||
# drwxr-xr-x 10003 kuangfangjun root 0 Aug 26 17:45 Wave
|
||||
|
||||
# If you have trouble accessing huggingface.co, please use
|
||||
#
|
||||
# cd $dl_dir
|
||||
# wget https://huggingface.co/openspeech/BZNSYP/resolve/main/BZNSYP.tar.bz2
|
||||
# tar xf BZNSYP.tar.bz2
|
||||
# cd ..
|
||||
|
||||
# If you have pre-downloaded it to /path/to/BZNSYP, you can create a symlink
|
||||
#
|
||||
# ln -sfv /path/to/BZNSYP $dl_dir/BZNSYP
|
||||
#
|
||||
if [ ! -d $dl_dir/BZNSYP/Wave ]; then
|
||||
lhotse download baker-zh $dl_dir
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
log "Stage 1: Prepare baker-zh manifest"
|
||||
# We assume that you have downloaded the baker corpus
|
||||
# to $dl_dir/BZNSYP
|
||||
mkdir -p data/manifests
|
||||
if [ ! -e data/manifests/.baker-zh.done ]; then
|
||||
lhotse prepare baker-zh $dl_dir/BZNSYP data/manifests
|
||||
touch data/manifests/.baker-zh.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
log "Stage 2: Generate tokens.txt"
|
||||
if [ ! -e data/tokens.txt ]; then
|
||||
python3 ./local/generate_tokens.py --tokens data/tokens.txt
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
|
||||
log "Stage 3: Generate raw cutset"
|
||||
if [ ! -e data/manifests/baker_zh_cuts_raw.jsonl.gz ]; then
|
||||
lhotse cut simple \
|
||||
-r ./data/manifests/baker_zh_recordings_all.jsonl.gz \
|
||||
-s ./data/manifests/baker_zh_supervisions_all.jsonl.gz \
|
||||
./data/manifests/baker_zh_cuts_raw.jsonl.gz
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
log "Stage 4: Convert text to tokens"
|
||||
if [ ! -e data/manifests/baker_zh_cuts.jsonl.gz ]; then
|
||||
python3 ./local/convert_text_to_tokens.py \
|
||||
--in-file ./data/manifests/baker_zh_cuts_raw.jsonl.gz \
|
||||
--out-file ./data/manifests/baker_zh_cuts.jsonl.gz
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Generate fbank (used by ./matcha)"
|
||||
mkdir -p data/fbank
|
||||
if [ ! -e data/fbank/.baker-zh.done ]; then
|
||||
./local/compute_fbank_baker_zh.py
|
||||
touch data/fbank/.baker-zh.done
|
||||
fi
|
||||
|
||||
if [ ! -e data/fbank/.baker-zh-validated.done ]; then
|
||||
log "Validating data/fbank for baker-zh (used by ./matcha)"
|
||||
python3 ./local/validate_manifest.py \
|
||||
data/fbank/baker_zh_cuts.jsonl.gz
|
||||
touch data/fbank/.baker-zh-validated.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Split the baker-zh cuts into train, valid and test sets (used by ./matcha)"
|
||||
if [ ! -e data/fbank/.baker_zh_split.done ]; then
|
||||
lhotse subset --last 600 \
|
||||
data/fbank/baker_zh_cuts.jsonl.gz \
|
||||
data/fbank/baker_zh_cuts_validtest.jsonl.gz
|
||||
lhotse subset --first 100 \
|
||||
data/fbank/baker_zh_cuts_validtest.jsonl.gz \
|
||||
data/fbank/baker_zh_cuts_valid.jsonl.gz
|
||||
lhotse subset --last 500 \
|
||||
data/fbank/baker_zh_cuts_validtest.jsonl.gz \
|
||||
data/fbank/baker_zh_cuts_test.jsonl.gz
|
||||
|
||||
rm data/fbank/baker_zh_cuts_validtest.jsonl.gz
|
||||
|
||||
n=$(( $(gunzip -c data/fbank/baker_zh_cuts.jsonl.gz | wc -l) - 600 ))
|
||||
|
||||
lhotse subset --first $n \
|
||||
data/fbank/baker_zh_cuts.jsonl.gz \
|
||||
data/fbank/baker_zh_cuts_train.jsonl.gz
|
||||
|
||||
touch data/fbank/.baker_zh_split.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
||||
log "Stage 6: Compute fbank mean and std (used by ./matcha)"
|
||||
if [ ! -f ./data/fbank/cmvn.json ]; then
|
||||
./local/compute_fbank_statistics.py ./data/fbank/baker_zh_cuts_train.jsonl.gz ./data/fbank/cmvn.json
|
||||
fi
|
||||
fi
|
||||
1
egs/baker_zh/TTS/shared
Symbolic link
1
egs/baker_zh/TTS/shared
Symbolic link
@ -0,0 +1 @@
|
||||
../../../icefall/shared
|
||||
@ -166,7 +166,7 @@ To export the checkpoint to onnx:
|
||||
--tokens ./data/tokens.txt
|
||||
```
|
||||
|
||||
The above command generate the following files:
|
||||
The above command generates the following files:
|
||||
|
||||
- model-steps-2.onnx
|
||||
- model-steps-3.onnx
|
||||
|
||||
@ -93,14 +93,14 @@ class ModelWrapper(torch.nn.Module):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lengths: torch.Tensor,
|
||||
temperature: torch.Tensor,
|
||||
noise_scale: torch.Tensor,
|
||||
length_scale: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Args: :
|
||||
x: (batch_size, num_tokens), torch.int64
|
||||
x_lengths: (batch_size,), torch.int64
|
||||
temperature: (1,), torch.float32
|
||||
noise_scale: (1,), torch.float32
|
||||
length_scale (1,), torch.float32
|
||||
Returns:
|
||||
audio: (batch_size, num_samples)
|
||||
@ -110,7 +110,7 @@ class ModelWrapper(torch.nn.Module):
|
||||
x=x,
|
||||
x_lengths=x_lengths,
|
||||
n_timesteps=self.num_steps,
|
||||
temperature=temperature,
|
||||
temperature=noise_scale,
|
||||
length_scale=length_scale,
|
||||
)["mel"]
|
||||
# mel: (batch_size, feat_dim, num_frames)
|
||||
@ -127,7 +127,6 @@ def main():
|
||||
params.update(vars(args))
|
||||
|
||||
tokenizer = Tokenizer(params.tokens)
|
||||
params.blank_id = tokenizer.pad_id
|
||||
params.vocab_size = tokenizer.vocab_size
|
||||
params.model_args.n_vocab = params.vocab_size
|
||||
|
||||
@ -153,14 +152,14 @@ def main():
|
||||
# encoder has a large initial length
|
||||
x = torch.ones(1, 1000, dtype=torch.int64)
|
||||
x_lengths = torch.tensor([x.shape[1]], dtype=torch.int64)
|
||||
temperature = torch.tensor([1.0])
|
||||
noise_scale = torch.tensor([1.0])
|
||||
length_scale = torch.tensor([1.0])
|
||||
|
||||
opset_version = 14
|
||||
filename = f"model-steps-{num_steps}.onnx"
|
||||
torch.onnx.export(
|
||||
wrapper,
|
||||
(x, x_lengths, temperature, length_scale),
|
||||
(x, x_lengths, noise_scale, length_scale),
|
||||
filename,
|
||||
opset_version=opset_version,
|
||||
input_names=["x", "x_length", "noise_scale", "length_scale"],
|
||||
|
||||
@ -132,7 +132,7 @@ class OnnxModel:
|
||||
print("x_lengths", x_lengths)
|
||||
print("x", x.shape)
|
||||
|
||||
temperature = torch.tensor([1.0], dtype=torch.float32)
|
||||
noise_scale = torch.tensor([1.0], dtype=torch.float32)
|
||||
length_scale = torch.tensor([1.0], dtype=torch.float32)
|
||||
|
||||
mel = self.model.run(
|
||||
@ -140,7 +140,7 @@ class OnnxModel:
|
||||
{
|
||||
self.model.get_inputs()[0].name: x.numpy(),
|
||||
self.model.get_inputs()[1].name: x_lengths.numpy(),
|
||||
self.model.get_inputs()[2].name: temperature.numpy(),
|
||||
self.model.get_inputs()[2].name: noise_scale.numpy(),
|
||||
self.model.get_inputs()[3].name: length_scale.numpy(),
|
||||
},
|
||||
)[0]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user