Add MatchaTTS for the Chinese dataset Baker (#1849)

.github/scripts/baker_zh/TTS/run-matcha.sh

@@ -0,0 +1,167 @@
+#!/usr/bin/env bash
+
+set -ex
+
+apt-get update
+apt-get install -y sox
+
+python3 -m pip install numba conformer==0.3.2 diffusers librosa
+python3 -m pip install jieba
+
+
+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]}) $*"
+}
+
+cd egs/baker_zh/TTS
+
+sed -i.bak s/600/8/g ./prepare.sh
+sed -i.bak s/"first 100"/"first 3"/g ./prepare.sh
+sed -i.bak s/500/5/g ./prepare.sh
+git diff
+
+function prepare_data() {
+  # We have created a subset of the data for testing
+  #
+  mkdir -p download
+  pushd download
+  wget -q https://huggingface.co/csukuangfj/tmp-files/resolve/main/BZNSYP-samples.tar.bz2
+  tar xvf BZNSYP-samples.tar.bz2
+  mv BZNSYP-samples BZNSYP
+  rm BZNSYP-samples.tar.bz2
+  popd
+
+  ./prepare.sh
+  tree .
+}
+
+function train() {
+  pushd ./matcha
+  sed -i.bak s/1500/3/g ./train.py
+  git diff .
+  popd
+
+  ./matcha/train.py \
+    --exp-dir matcha/exp \
+    --num-epochs 1 \
+    --save-every-n 1 \
+    --num-buckets 2 \
+    --tokens data/tokens.txt \
+    --max-duration 20
+
+    ls -lh matcha/exp
+}
+
+function infer() {
+  curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+
+  ./matcha/infer.py \
+    --num-buckets 2 \
+    --epoch 1 \
+    --exp-dir ./matcha/exp \
+    --tokens data/tokens.txt \
+    --cmvn ./data/fbank/cmvn.json \
+    --vocoder ./generator_v2 \
+    --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
+
+  ls -lh *.wav
+  soxi ./generated.wav
+  rm -v ./generated.wav
+  rm -v generator_v2
+}
+
+function export_onnx() {
+  pushd matcha/exp
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/epoch-2000.pt
+  popd
+
+  pushd data/fbank
+  rm -v *.json
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/cmvn.json
+  popd
+
+  ./matcha/export_onnx.py \
+    --exp-dir ./matcha/exp \
+    --epoch 2000 \
+    --tokens ./data/tokens.txt \
+    --cmvn ./data/fbank/cmvn.json
+
+  ls -lh *.onnx
+
+  if false; then
+    # The CI machine does not have enough memory to run it
+    #
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
+    python3 ./matcha/export_onnx_hifigan.py
+  else
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v1.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v2.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v3.onnx
+  fi
+
+  ls -lh *.onnx
+
+  python3 ./matcha/generate_lexicon.py
+
+  for v in v1 v2 v3; do
+    python3 ./matcha/onnx_pretrained.py \
+     --acoustic-model ./model-steps-6.onnx \
+     --vocoder ./hifigan_$v.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 /icefall/generated-matcha-tts-steps-6-$v.wav
+  done
+
+  ls -lh /icefall/*.wav
+  soxi /icefall/generated-matcha-tts-steps-6-*.wav
+  cp ./model-steps-*.onnx /icefall
+
+  d=matcha-icefall-zh-baker
+  mkdir $d
+  cp -v data/tokens.txt $d
+  cp -v lexicon.txt $d
+  cp model-steps-3.onnx $d
+  pushd $d
+  curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2
+  tar xvf dict.tar.bz2
+  rm dict.tar.bz2
+
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/date.fst
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/number.fst
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/phone.fst
+
+cat >README.md <<EOF
+# Introduction
+
+This model is trained using 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.
+
+You can find the training code at
+https://github.com/k2-fsa/icefall/tree/master/egs/baker_zh/TTS
+EOF
+
+  ls -lh
+  popd
+  tar cvjf $d.tar.bz2 $d
+  mv $d.tar.bz2 /icefall
+  mv $d /icefall
+}
+
+prepare_data
+train
+infer
+export_onnx
+
+rm -rfv generator_v* matcha/exp
+git checkout .

.github/scripts/docker/generate_build_matrix.py

@@ -2,9 +2,19 @@
 # Copyright    2023  Xiaomi Corp.        (authors: Fangjun Kuang)
 
 
+import argparse
 import json
 
 
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--min-torch-version",
+        help="Minimu torch version",
+    )
+    return parser.parse_args()
+
+
 def version_gt(a, b):
     a_major, a_minor = list(map(int, a.split(".")))[:2]
     b_major, b_minor = list(map(int, b.split(".")))[:2]
@@ -42,7 +52,7 @@ def get_torchaudio_version(torch_version):
         return torch_version
 
 
-def get_matrix():
+def get_matrix(min_torch_version):
     k2_version = "1.24.4.dev20241029"
     kaldifeat_version = "1.25.5.dev20241029"
     version = "20241218"
@@ -64,6 +74,9 @@ def get_matrix():
     matrix = []
     for p in python_version:
         for t in torch_version:
+            if min_torch_version and version_gt(min_torch_version, t):
+                continue
+
             # torchaudio <= 1.13.x supports only python <= 3.10
 
             if version_gt(p, "3.10") and not version_gt(t, "2.0"):
@@ -101,7 +114,8 @@ def get_matrix():
 
 
 def main():
-    matrix = get_matrix()
+    args = get_args()
+    matrix = get_matrix(min_torch_version=args.min_torch_version)
     print(json.dumps({"include": matrix}))
 
 

.github/scripts/ljspeech/TTS/run-matcha.sh

@@ -90,7 +90,7 @@ function export_onnx() {
   ls -lh *.onnx
 
   if false; then
-    # THe CI machine does not have enough memory to run it
+    # The CI machine does not have enough memory to run it
     #
     curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
     curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2

.github/workflows/baker_zh.yml

@@ -0,0 +1,152 @@
+name: baker_zh
+
+on:
+  push:
+    branches:
+      - master
+      - baker-matcha-2
+
+  pull_request:
+    branches:
+      - master
+
+  workflow_dispatch:
+
+concurrency:
+  group: baker-zh-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  generate_build_matrix:
+    if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
+    # see https://github.com/pytorch/pytorch/pull/50633
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Generating build matrix
+        id: set-matrix
+        run: |
+          # outputting for debugging purposes
+          python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
+          echo "::set-output name=matrix::${MATRIX}"
+
+  baker_zh:
+    needs: generate_build_matrix
+    name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        ${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Free space
+        shell: bash
+        run: |
+          ls -lh
+          df -h
+          rm -rf /opt/hostedtoolcache
+          df -h
+          echo "pwd: $PWD"
+          echo "github.workspace ${{ github.workspace }}"
+
+      - name: Run tests
+        uses: addnab/docker-run-action@v3
+        with:
+            image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
+            options: |
+              --volume ${{ github.workspace }}/:/icefall
+            shell: bash
+            run: |
+              export PYTHONPATH=/icefall:$PYTHONPATH
+              cd /icefall
+
+              pip install onnx==1.17.0
+
+              pip list
+
+              git config --global --add safe.directory /icefall
+
+              .github/scripts/baker_zh/TTS/run-matcha.sh
+
+      - name: display files
+        shell: bash
+        run: |
+          ls -lh
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
+          path: ./*.wav
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-2
+          path: ./model-steps-2.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-3
+          path: ./model-steps-3.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-4
+          path: ./model-steps-4.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-5
+          path: ./model-steps-5.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-6
+          path: ./model-steps-6.onnx
+
+      - name: Upload models to huggingface
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        shell: bash
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          d=matcha-icefall-zh-baker
+
+          GIT_LFS_SKIP_SMUDGE=1  git clone https://huggingface.co/csukuangfj/$d hf
+          cp -av $d/* hf/
+
+          pushd hf
+          git add .
+
+          git config --global user.name "csukuangfj"
+          git config --global user.email "csukuangfj@gmail.com"
+          git config --global lfs.allowincompletepush true
+
+          git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$d main || true
+          popd
+
+      - 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 }}
+          tag: tts-models

egs/baker_zh/TTS/.gitignore

@@ -0,0 +1,6 @@
+path.sh
+*.onnx
+*.wav
+generator_v1
+generator_v2
+generator_v3

egs/baker_zh/TTS/README.md

@@ -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
+

egs/baker_zh/TTS/local/audio.py

@@ -0,0 +1 @@
+../matcha/audio.py

egs/baker_zh/TTS/local/compute_fbank_baker_zh.py

@@ -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)

egs/baker_zh/TTS/local/compute_fbank_statistics.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/local/compute_fbank_statistics.py

egs/baker_zh/TTS/local/convert_text_to_tokens.py

@@ -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()

egs/baker_zh/TTS/local/fbank.py

@@ -0,0 +1 @@
+../matcha/fbank.py

egs/baker_zh/TTS/local/generate_tokens.py

@@ -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()

egs/baker_zh/TTS/local/validate_manifest.py

@@ -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()

egs/baker_zh/TTS/matcha/audio.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/audio.py

egs/baker_zh/TTS/matcha/export_onnx.py

@@ -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()

egs/baker_zh/TTS/matcha/export_onnx_hifigan.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/export_onnx_hifigan.py

egs/baker_zh/TTS/matcha/fbank.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/fbank.py

egs/baker_zh/TTS/matcha/generate_lexicon.py

@@ -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()

egs/baker_zh/TTS/matcha/hifigan

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/hifigan

egs/baker_zh/TTS/matcha/infer.py

@@ -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()

egs/baker_zh/TTS/matcha/model.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/model.py

egs/baker_zh/TTS/matcha/models

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/models

egs/baker_zh/TTS/matcha/monotonic_align

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/monotonic_align

egs/baker_zh/TTS/matcha/onnx_pretrained.py

@@ -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         |
+
+"""

egs/baker_zh/TTS/matcha/tokenizer.py

@@ -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()

egs/baker_zh/TTS/matcha/train.py

@@ -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()

egs/baker_zh/TTS/matcha/tts_datamodule.py

@@ -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"
+        )

egs/baker_zh/TTS/matcha/utils.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/utils.py

egs/baker_zh/TTS/prepare.sh

@@ -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

egs/baker_zh/TTS/shared

@@ -0,0 +1 @@
+../../../icefall/shared

egs/ljspeech/TTS/README.md

@@ -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

egs/ljspeech/TTS/matcha/export_onnx.py

@@ -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"],

egs/ljspeech/TTS/matcha/onnx_pretrained.py

@@ -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]