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Merge with master
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pkufool
commit
802bf98f59
117
.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh
vendored
Executable file
117
.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh
vendored
Executable file
@ -0,0 +1,117 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -e
|
||||
|
||||
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/librispeech/ASR
|
||||
|
||||
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-transducer-ctc-2023-06-13
|
||||
|
||||
log "Downloading pre-trained model from $repo_url"
|
||||
git lfs install
|
||||
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
|
||||
repo=$(basename $repo_url)
|
||||
|
||||
log "Display test files"
|
||||
tree $repo/
|
||||
ls -lh $repo/test_wavs/*.wav
|
||||
|
||||
pushd $repo/exp
|
||||
git lfs pull --include "data/lang_bpe_500/bpe.model"
|
||||
git lfs pull --include "data/lang_bpe_500/HLG.pt"
|
||||
git lfs pull --include "data/lang_bpe_500/L.pt"
|
||||
git lfs pull --include "data/lang_bpe_500/LG.pt"
|
||||
git lfs pull --include "data/lang_bpe_500/Linv.pt"
|
||||
git lfs pull --include "data/lm/G_4_gram.pt"
|
||||
git lfs pull --include "exp/jit_script.pt"
|
||||
git lfs pull --include "exp/pretrained.pt"
|
||||
ln -s pretrained.pt epoch-99.pt
|
||||
ls -lh *.pt
|
||||
popd
|
||||
|
||||
log "Export to torchscript model"
|
||||
./zipformer/export.py \
|
||||
--exp-dir $repo/exp \
|
||||
--use-transducer 1 \
|
||||
--use-ctc 1 \
|
||||
--use-averaged-model false \
|
||||
--bpe-model $repo/data/lang_bpe_500/bpe.model \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--jit 1
|
||||
|
||||
ls -lh $repo/exp/*.pt
|
||||
|
||||
log "Decode with models exported by torch.jit.script()"
|
||||
|
||||
for method in ctc-decoding 1best; do
|
||||
./zipformer/jit_pretrained_ctc.py \
|
||||
--bpe-model $repo/data/lang_bpe_500/bpe.model \
|
||||
--model-filename $repo/exp/jit_script.pt \
|
||||
--HLG $repo/data/lang_bpe_500/HLG.pt \
|
||||
--words-file $repo/data/lang_bpe_500/words.txt \
|
||||
--G $repo/data/lm/G_4_gram.pt \
|
||||
--method $method \
|
||||
--sample-rate 16000 \
|
||||
$repo/test_wavs/1089-134686-0001.wav \
|
||||
$repo/test_wavs/1221-135766-0001.wav \
|
||||
$repo/test_wavs/1221-135766-0002.wav
|
||||
done
|
||||
|
||||
for method in ctc-decoding 1best; do
|
||||
log "$method"
|
||||
|
||||
./zipformer/pretrained_ctc.py \
|
||||
--use-transducer 1 \
|
||||
--use-ctc 1 \
|
||||
--method $method \
|
||||
--checkpoint $repo/exp/pretrained.pt \
|
||||
--bpe-model $repo/data/lang_bpe_500/bpe.model \
|
||||
--words-file $repo/data/lang_bpe_500/words.txt \
|
||||
--HLG $repo/data/lang_bpe_500/HLG.pt \
|
||||
--G $repo/data/lm/G_4_gram.pt \
|
||||
--words-file $repo/data/lang_bpe_500/words.txt \
|
||||
--sample-rate 16000 \
|
||||
$repo/test_wavs/1089-134686-0001.wav \
|
||||
$repo/test_wavs/1221-135766-0001.wav \
|
||||
$repo/test_wavs/1221-135766-0002.wav
|
||||
done
|
||||
|
||||
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
|
||||
echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
|
||||
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then
|
||||
mkdir -p zipformer/exp
|
||||
ln -s $PWD/$repo/exp/pretrained.pt zipformer/exp/epoch-999.pt
|
||||
ln -s $PWD/$repo/data/lang_bpe_500 data/
|
||||
|
||||
ls -lh data
|
||||
ls -lh zipformer/exp
|
||||
|
||||
log "Decoding test-clean and test-other"
|
||||
|
||||
# use a small value for decoding with CPU
|
||||
max_duration=100
|
||||
|
||||
for method in ctc-decoding 1best; do
|
||||
log "Decoding with $method"
|
||||
|
||||
./zipformer/ctc_decode.py \
|
||||
--use-transducer 1 \
|
||||
--use-ctc 1 \
|
||||
--decoding-method $method \
|
||||
--nbest-scale 1.0 \
|
||||
--hlg-scale 0.6 \
|
||||
--epoch 999 \
|
||||
--avg 1 \
|
||||
--use-averaged-model 0 \
|
||||
--max-duration $max_duration \
|
||||
--exp-dir zipformer/exp
|
||||
done
|
||||
|
||||
rm zipformer/exp/*.pt
|
||||
fi
|
||||
155
.github/workflows/run-librispeech-zipformer-ctc-2023-06-14.yml
vendored
Normal file
155
.github/workflows/run-librispeech-zipformer-ctc-2023-06-14.yml
vendored
Normal file
@ -0,0 +1,155 @@
|
||||
# Copyright 2022 Fangjun Kuang (csukuangfj@gmail.com)
|
||||
|
||||
# See ../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
name: run-librispeech-zipformer-ctc-2023-06-14
|
||||
# zipformer
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
pull_request:
|
||||
types: [labeled]
|
||||
|
||||
schedule:
|
||||
# minute (0-59)
|
||||
# hour (0-23)
|
||||
# day of the month (1-31)
|
||||
# month (1-12)
|
||||
# day of the week (0-6)
|
||||
# nightly build at 15:50 UTC time every day
|
||||
- cron: "50 15 * * *"
|
||||
|
||||
concurrency:
|
||||
group: run_librispeech_2023_06_14_zipformer-ctc-${{ github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
run_librispeech_2023_06_14_zipformer_ctc:
|
||||
if: github.event.label.name == 'zipformer' ||github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
os: [ubuntu-latest]
|
||||
python-version: [3.8]
|
||||
|
||||
fail-fast: false
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Setup Python ${{ matrix.python-version }}
|
||||
uses: actions/setup-python@v2
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
cache: 'pip'
|
||||
cache-dependency-path: '**/requirements-ci.txt'
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
|
||||
pip uninstall -y protobuf
|
||||
pip install --no-binary protobuf protobuf==3.20.*
|
||||
|
||||
- name: Cache kaldifeat
|
||||
id: my-cache
|
||||
uses: actions/cache@v2
|
||||
with:
|
||||
path: |
|
||||
~/tmp/kaldifeat
|
||||
key: cache-tmp-${{ matrix.python-version }}-2023-05-22
|
||||
|
||||
- name: Install kaldifeat
|
||||
if: steps.my-cache.outputs.cache-hit != 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
.github/scripts/install-kaldifeat.sh
|
||||
|
||||
- name: Cache LibriSpeech test-clean and test-other datasets
|
||||
id: libri-test-clean-and-test-other-data
|
||||
uses: actions/cache@v2
|
||||
with:
|
||||
path: |
|
||||
~/tmp/download
|
||||
key: cache-libri-test-clean-and-test-other
|
||||
|
||||
- name: Download LibriSpeech test-clean and test-other
|
||||
if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
.github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
|
||||
|
||||
- name: Prepare manifests for LibriSpeech test-clean and test-other
|
||||
shell: bash
|
||||
run: |
|
||||
.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
|
||||
|
||||
- name: Cache LibriSpeech test-clean and test-other fbank features
|
||||
id: libri-test-clean-and-test-other-fbank
|
||||
uses: actions/cache@v2
|
||||
with:
|
||||
path: |
|
||||
~/tmp/fbank-libri
|
||||
key: cache-libri-fbank-test-clean-and-test-other-v2
|
||||
|
||||
- name: Compute fbank for LibriSpeech test-clean and test-other
|
||||
if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
|
||||
shell: bash
|
||||
run: |
|
||||
.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
|
||||
|
||||
- name: Inference with pre-trained model
|
||||
shell: bash
|
||||
env:
|
||||
GITHUB_EVENT_NAME: ${{ github.event_name }}
|
||||
GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
|
||||
run: |
|
||||
mkdir -p egs/librispeech/ASR/data
|
||||
ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
|
||||
ls -lh egs/librispeech/ASR/data/*
|
||||
|
||||
sudo apt-get -qq install git-lfs tree
|
||||
export PYTHONPATH=$PWD:$PYTHONPATH
|
||||
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
|
||||
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
|
||||
|
||||
.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh
|
||||
|
||||
- name: Display decoding results for librispeech zipformer
|
||||
if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
|
||||
shell: bash
|
||||
run: |
|
||||
cd egs/librispeech/ASR/
|
||||
tree ./zipformer/exp
|
||||
|
||||
cd zipformer
|
||||
echo "results for zipformer"
|
||||
echo "===ctc-decoding==="
|
||||
find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
|
||||
find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
|
||||
|
||||
echo "===1best==="
|
||||
find exp/1best -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
|
||||
find exp/1best -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
|
||||
|
||||
- name: Upload decoding results for librispeech zipformer
|
||||
uses: actions/upload-artifact@v2
|
||||
if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
|
||||
with:
|
||||
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-zipformer-2022-11-11
|
||||
path: egs/librispeech/ASR/zipformer/exp/
|
||||
@ -47,6 +47,7 @@ We place an additional Conv1d layer right after the input embedding layer.
|
||||
| `conformer-ctc` | Conformer | Use auxiliary attention head |
|
||||
| `conformer-ctc2` | Reworked Conformer | Use auxiliary attention head |
|
||||
| `conformer-ctc3` | Reworked Conformer | Streaming version + delay penalty |
|
||||
| `zipformer` | Upgraded Zipformer | Use auxiliary transducer head | The latest recipe |
|
||||
|
||||
# MMI
|
||||
|
||||
|
||||
@ -1,5 +1,69 @@
|
||||
## Results
|
||||
|
||||
### zipformer (zipformer + pruned stateless transducer + CTC)
|
||||
|
||||
See <https://github.com/k2-fsa/icefall/pull/1111> for more details.
|
||||
|
||||
[zipformer](./zipformer)
|
||||
|
||||
#### Non-streaming
|
||||
|
||||
##### normal-scaled model, number of model parameters: 65805511, i.e., 65.81 M
|
||||
|
||||
The tensorboard log can be found at
|
||||
<https://tensorboard.dev/experiment/Lo3Qlad7TP68ulM2K0ixgQ/>
|
||||
|
||||
You can find a pretrained model, training logs, decoding logs, and decoding results at:
|
||||
<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-transducer-ctc-2023-06-13>
|
||||
|
||||
You can use <https://github.com/k2-fsa/sherpa> to deploy it.
|
||||
|
||||
Results of the CTC head:
|
||||
|
||||
| decoding method | test-clean | test-other | comment |
|
||||
|-------------------------|------------|------------|--------------------|
|
||||
| ctc-decoding | 2.40 | 5.66 | --epoch 40 --avg 16 |
|
||||
| 1best | 2.46 | 5.11 | --epoch 40 --avg 16 |
|
||||
| nbest | 2.46 | 5.11 | --epoch 40 --avg 16 |
|
||||
| nbest-rescoring | 2.37 | 4.93 | --epoch 40 --avg 16 |
|
||||
| whole-lattice-rescoring | 2.37 | 4.88 | --epoch 40 --avg 16 |
|
||||
|
||||
The training command is:
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES="0,1,2,3"
|
||||
./zipformer/train.py \
|
||||
--world-size 4 \
|
||||
--num-epochs 40 \
|
||||
--start-epoch 1 \
|
||||
--use-fp16 1 \
|
||||
--exp-dir zipformer/exp-ctc-rnnt \
|
||||
--causal 0 \
|
||||
--use-transducer 1 \
|
||||
--use-ctc 1 \
|
||||
--ctc-loss-scale 0.2 \
|
||||
--full-libri 1 \
|
||||
--max-duration 1000
|
||||
```
|
||||
|
||||
The decoding command is:
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES="0"
|
||||
for m in ctc-decoding 1best nbest nbest-rescoring whole-lattice-rescoring; do
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 40 \
|
||||
--avg 16 \
|
||||
--exp-dir zipformer/exp-ctc-rnnt \
|
||||
--use-transducer 1 \
|
||||
--use-ctc 1 \
|
||||
--max-duration 300 \
|
||||
--causal 0 \
|
||||
--num-paths 100 \
|
||||
--nbest-scale 1.0 \
|
||||
--hlg-scale 0.6 \
|
||||
--decoding-method $m
|
||||
done
|
||||
```
|
||||
|
||||
### zipformer (zipformer + pruned stateless transducer)
|
||||
|
||||
See <https://github.com/k2-fsa/icefall/pull/1058> for more details.
|
||||
@ -285,7 +349,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
|
||||
--lr-epochs 100 \
|
||||
--lr-batches 100000 \
|
||||
--bpe-model icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/data/lang_bpe_500/bpe.model \
|
||||
--do-finetune True \
|
||||
--do-finetune True \
|
||||
--use-mux True \
|
||||
--finetune-ckpt icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/exp/pretrain.pt \
|
||||
--max-duration 500
|
||||
|
||||
@ -22,7 +22,7 @@ from typing import Dict, List, Optional, Tuple, Union
|
||||
import k2
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
from model import Transducer
|
||||
from torch import nn
|
||||
|
||||
from icefall import ContextGraph, ContextState, NgramLm, NgramLmStateCost
|
||||
from icefall.decode import Nbest, one_best_decoding
|
||||
@ -39,7 +39,7 @@ from icefall.utils import (
|
||||
|
||||
|
||||
def fast_beam_search_one_best(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -103,7 +103,7 @@ def fast_beam_search_one_best(
|
||||
|
||||
|
||||
def fast_beam_search_nbest_LG(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -233,7 +233,7 @@ def fast_beam_search_nbest_LG(
|
||||
|
||||
|
||||
def fast_beam_search_nbest(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -325,7 +325,7 @@ def fast_beam_search_nbest(
|
||||
|
||||
|
||||
def fast_beam_search_nbest_oracle(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -432,7 +432,7 @@ def fast_beam_search_nbest_oracle(
|
||||
|
||||
|
||||
def fast_beam_search(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -537,7 +537,7 @@ def fast_beam_search(
|
||||
|
||||
|
||||
def greedy_search(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
max_sym_per_frame: int,
|
||||
blank_penalty: float = 0.0,
|
||||
@ -641,7 +641,7 @@ def greedy_search(
|
||||
|
||||
|
||||
def greedy_search_batch(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
blank_penalty: float = 0,
|
||||
@ -940,7 +940,7 @@ def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
|
||||
|
||||
|
||||
def modified_beam_search(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
context_graph: Optional[ContextGraph] = None,
|
||||
@ -1146,7 +1146,7 @@ def modified_beam_search(
|
||||
|
||||
|
||||
def modified_beam_search_lm_rescore(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
LM: LmScorer,
|
||||
@ -1344,7 +1344,7 @@ def modified_beam_search_lm_rescore(
|
||||
|
||||
|
||||
def modified_beam_search_lm_rescore_LODR(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
LM: LmScorer,
|
||||
@ -1560,7 +1560,7 @@ def modified_beam_search_lm_rescore_LODR(
|
||||
|
||||
|
||||
def _deprecated_modified_beam_search(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
beam: int = 4,
|
||||
return_timestamps: bool = False,
|
||||
@ -1685,7 +1685,7 @@ def _deprecated_modified_beam_search(
|
||||
|
||||
|
||||
def beam_search(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
beam: int = 4,
|
||||
temperature: float = 1.0,
|
||||
@ -1849,7 +1849,7 @@ def beam_search(
|
||||
|
||||
|
||||
def fast_beam_search_with_nbest_rescoring(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -2009,7 +2009,7 @@ def fast_beam_search_with_nbest_rescoring(
|
||||
|
||||
|
||||
def fast_beam_search_with_nbest_rnn_rescoring(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
decoding_graph: k2.Fsa,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
@ -2200,7 +2200,7 @@ def fast_beam_search_with_nbest_rnn_rescoring(
|
||||
|
||||
|
||||
def modified_beam_search_ngram_rescoring(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
ngram_lm: NgramLm,
|
||||
@ -2364,7 +2364,7 @@ def modified_beam_search_ngram_rescoring(
|
||||
|
||||
|
||||
def modified_beam_search_LODR(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
LODR_lm: NgramLm,
|
||||
@ -2635,7 +2635,7 @@ def modified_beam_search_LODR(
|
||||
|
||||
|
||||
def modified_beam_search_lm_shallow_fusion(
|
||||
model: Transducer,
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
LM: LmScorer,
|
||||
|
||||
847
egs/librispeech/ASR/zipformer/ctc_decode.py
Executable file
847
egs/librispeech/ASR/zipformer/ctc_decode.py
Executable file
@ -0,0 +1,847 @@
|
||||
#!/usr/bin/env python3
|
||||
#
|
||||
# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Liyong Guo,
|
||||
# Quandong Wang,
|
||||
# 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.
|
||||
"""
|
||||
Usage:
|
||||
|
||||
(1) ctc-decoding
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--decoding-method ctc-decoding
|
||||
|
||||
(2) 1best
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--decoding-method 1best
|
||||
|
||||
(3) nbest
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--decoding-method nbest
|
||||
|
||||
(4) nbest-rescoring
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--nbest-scale 1.0 \
|
||||
--lm-dir data/lm \
|
||||
--decoding-method nbest-rescoring
|
||||
|
||||
(5) whole-lattice-rescoring
|
||||
./zipformer/ctc_decode.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--max-duration 600 \
|
||||
--hlg-scale 0.6 \
|
||||
--nbest-scale 1.0 \
|
||||
--lm-dir data/lm \
|
||||
--decoding-method whole-lattice-rescoring
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import LibriSpeechAsrDataModule
|
||||
from train import add_model_arguments, get_params, get_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
nbest_decoding,
|
||||
nbest_oracle,
|
||||
one_best_decoding,
|
||||
rescore_with_n_best_list,
|
||||
rescore_with_whole_lattice,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
get_texts,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=30,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
You can specify --avg to use more checkpoints for model averaging.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--iter",
|
||||
type=int,
|
||||
default=0,
|
||||
help="""If positive, --epoch is ignored and it
|
||||
will use the checkpoint exp_dir/checkpoint-iter.pt.
|
||||
You can specify --avg to use more checkpoints for model averaging.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch' and '--iter'",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-averaged-model",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Whether to load averaged model. Currently it only supports "
|
||||
"using --epoch. If True, it would decode with the averaged model "
|
||||
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
|
||||
"Actually only the models with epoch number of `epoch-avg` and "
|
||||
"`epoch` are loaded for averaging. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="zipformer/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bpe_500/bpe.model",
|
||||
help="Path to the BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default="data/lang_bpe_500",
|
||||
help="The lang dir containing word table and LG graph",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--context-size",
|
||||
type=int,
|
||||
default=2,
|
||||
help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="ctc-decoding",
|
||||
help="""Decoding method.
|
||||
Supported values are:
|
||||
- (1) ctc-decoding. Use CTC decoding. It uses a sentence piece
|
||||
model, i.e., lang_dir/bpe.model, to convert word pieces to words.
|
||||
It needs neither a lexicon nor an n-gram LM.
|
||||
- (2) 1best. Extract the best path from the decoding lattice as the
|
||||
decoding result.
|
||||
- (3) nbest. Extract n paths from the decoding lattice; the path
|
||||
with the highest score is the decoding result.
|
||||
- (4) nbest-rescoring. Extract n paths from the decoding lattice,
|
||||
rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
|
||||
the highest score is the decoding result.
|
||||
- (5) whole-lattice-rescoring. Rescore the decoding lattice with an
|
||||
n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
|
||||
is the decoding result.
|
||||
you have trained an RNN LM using ./rnn_lm/train.py
|
||||
- (6) nbest-oracle. Its WER is the lower bound of any n-best
|
||||
rescoring method can achieve. Useful for debugging n-best
|
||||
rescoring method.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""Number of paths for n-best based decoding method.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""The scale to be applied to `lattice.scores`.
|
||||
It's needed if you use any kinds of n-best based rescoring.
|
||||
Used only when "method" is one of the following values:
|
||||
nbest, nbest-rescoring, and nbest-oracle
|
||||
A smaller value results in more unique paths.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--hlg-scale",
|
||||
type=float,
|
||||
default=0.6,
|
||||
help="""The scale to be applied to `hlg.scores`.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lm-dir",
|
||||
type=str,
|
||||
default="data/lm",
|
||||
help="""The n-gram LM dir.
|
||||
It should contain either G_4_gram.pt or G_4_gram.fst.txt
|
||||
""",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def get_decoding_params() -> AttributeDict:
|
||||
"""Parameters for decoding."""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"frame_shift_ms": 10,
|
||||
"search_beam": 20,
|
||||
"output_beam": 8,
|
||||
"min_active_states": 30,
|
||||
"max_active_states": 10000,
|
||||
"use_double_scores": True,
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
HLG: Optional[k2.Fsa],
|
||||
H: Optional[k2.Fsa],
|
||||
bpe_model: Optional[spm.SentencePieceProcessor],
|
||||
batch: dict,
|
||||
word_table: k2.SymbolTable,
|
||||
G: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[List[str]]]:
|
||||
"""Decode one batch and return the result in a dict. The dict has the
|
||||
following format:
|
||||
- key: It indicates the setting used for decoding. For example,
|
||||
if no rescoring is used, the key is the string `no_rescore`.
|
||||
If LM rescoring is used, the key is the string `lm_scale_xxx`,
|
||||
where `xxx` is the value of `lm_scale`. An example key is
|
||||
`lm_scale_0.7`
|
||||
- value: It contains the decoding result. `len(value)` equals to
|
||||
batch size. `value[i]` is the decoding result for the i-th
|
||||
utterance in the given batch.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
|
||||
- params.decoding_method is "1best", it uses 1best decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest", it uses nbest decoding without LM rescoring.
|
||||
- params.decoding_method is "nbest-rescoring", it uses nbest LM rescoring.
|
||||
- params.decoding_method is "whole-lattice-rescoring", it uses whole lattice LM
|
||||
rescoring.
|
||||
|
||||
model:
|
||||
The neural model.
|
||||
HLG:
|
||||
The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
|
||||
H:
|
||||
The ctc topo. Used only when params.decoding_method is ctc-decoding.
|
||||
bpe_model:
|
||||
The BPE model. Used only when params.decoding_method is ctc-decoding.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
G:
|
||||
An LM. It is not None when params.decoding_method is "nbest-rescoring"
|
||||
or "whole-lattice-rescoring". In general, the G in HLG
|
||||
is a 3-gram LM, while this G is a 4-gram LM.
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict. Note: If it decodes to nothing, then return None.
|
||||
"""
|
||||
if HLG is not None:
|
||||
device = HLG.device
|
||||
else:
|
||||
device = H.device
|
||||
feature = batch["inputs"]
|
||||
assert feature.ndim == 3
|
||||
feature = feature.to(device)
|
||||
# at entry, feature is (N, T, C)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
if params.causal:
|
||||
# this seems to cause insertions at the end of the utterance if used with zipformer.
|
||||
pad_len = 30
|
||||
feature_lens += pad_len
|
||||
feature = torch.nn.functional.pad(
|
||||
feature,
|
||||
pad=(0, 0, 0, pad_len),
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
|
||||
ctc_output = model.ctc_output(encoder_out) # (N, T, C)
|
||||
|
||||
supervision_segments = torch.stack(
|
||||
(
|
||||
supervisions["sequence_idx"],
|
||||
torch.div(
|
||||
supervisions["start_frame"],
|
||||
params.subsampling_factor,
|
||||
rounding_mode="floor",
|
||||
),
|
||||
torch.div(
|
||||
supervisions["num_frames"],
|
||||
params.subsampling_factor,
|
||||
rounding_mode="floor",
|
||||
),
|
||||
),
|
||||
1,
|
||||
).to(torch.int32)
|
||||
|
||||
if H is None:
|
||||
assert HLG is not None
|
||||
decoding_graph = HLG
|
||||
else:
|
||||
assert HLG is None
|
||||
assert bpe_model is not None
|
||||
decoding_graph = H
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=decoding_graph,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if params.decoding_method == "ctc-decoding":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
# Note: `best_path.aux_labels` contains token IDs, not word IDs
|
||||
# since we are using H, not HLG here.
|
||||
#
|
||||
# token_ids is a lit-of-list of IDs
|
||||
token_ids = get_texts(best_path)
|
||||
|
||||
# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
|
||||
hyps = bpe_model.decode(token_ids)
|
||||
|
||||
# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
|
||||
hyps = [s.split() for s in hyps]
|
||||
key = "ctc-decoding"
|
||||
return {key: hyps}
|
||||
|
||||
if params.decoding_method == "nbest-oracle":
|
||||
# Note: You can also pass rescored lattices to it.
|
||||
# We choose the HLG decoded lattice for speed reasons
|
||||
# as HLG decoding is faster and the oracle WER
|
||||
# is only slightly worse than that of rescored lattices.
|
||||
best_path = nbest_oracle(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
ref_texts=supervisions["text"],
|
||||
word_table=word_table,
|
||||
nbest_scale=params.nbest_scale,
|
||||
oov="<UNK>",
|
||||
)
|
||||
hyps = get_texts(best_path)
|
||||
hyps = [[word_table[i] for i in ids] for ids in hyps]
|
||||
key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa
|
||||
return {key: hyps}
|
||||
|
||||
if params.decoding_method in ["1best", "nbest"]:
|
||||
if params.decoding_method == "1best":
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
key = "no_rescore"
|
||||
else:
|
||||
best_path = nbest_decoding(
|
||||
lattice=lattice,
|
||||
num_paths=params.num_paths,
|
||||
use_double_scores=params.use_double_scores,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
|
||||
|
||||
hyps = get_texts(best_path)
|
||||
hyps = [[word_table[i] for i in ids] for ids in hyps]
|
||||
return {key: hyps}
|
||||
|
||||
assert params.decoding_method in [
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
]
|
||||
|
||||
lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
|
||||
lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
|
||||
lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
|
||||
|
||||
if params.decoding_method == "nbest-rescoring":
|
||||
best_path_dict = rescore_with_n_best_list(
|
||||
lattice=lattice,
|
||||
G=G,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=lm_scale_list,
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
elif params.decoding_method == "whole-lattice-rescoring":
|
||||
best_path_dict = rescore_with_whole_lattice(
|
||||
lattice=lattice,
|
||||
G_with_epsilon_loops=G,
|
||||
lm_scale_list=lm_scale_list,
|
||||
)
|
||||
else:
|
||||
assert False, f"Unsupported decoding method: {params.decoding_method}"
|
||||
|
||||
ans = dict()
|
||||
if best_path_dict is not None:
|
||||
for lm_scale_str, best_path in best_path_dict.items():
|
||||
hyps = get_texts(best_path)
|
||||
hyps = [[word_table[i] for i in ids] for ids in hyps]
|
||||
ans[lm_scale_str] = hyps
|
||||
else:
|
||||
ans = None
|
||||
return ans
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
HLG: Optional[k2.Fsa],
|
||||
H: Optional[k2.Fsa],
|
||||
bpe_model: Optional[spm.SentencePieceProcessor],
|
||||
word_table: k2.SymbolTable,
|
||||
G: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
PyTorch's dataloader containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
HLG:
|
||||
The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
|
||||
H:
|
||||
The ctc topo. Used only when params.decoding_method is ctc-decoding.
|
||||
bpe_model:
|
||||
The BPE model. Used only when params.decoding_method is ctc-decoding.
|
||||
word_table:
|
||||
It is the word symbol table.
|
||||
G:
|
||||
An LM. It is not None when params.decoding_method is "nbest-rescoring"
|
||||
or "whole-lattice-rescoring". In general, the G in HLG
|
||||
is a 3-gram LM, while this G is a 4-gram LM.
|
||||
Returns:
|
||||
Return a dict, whose key may be "no-rescore" if no LM rescoring
|
||||
is used, or it may be "lm_scale_0.7" if LM rescoring is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
HLG=HLG,
|
||||
H=H,
|
||||
bpe_model=bpe_model,
|
||||
batch=batch,
|
||||
word_table=word_table,
|
||||
G=G,
|
||||
)
|
||||
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((cut_id, ref_words, hyp_words))
|
||||
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
if batch_idx % 100 == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
|
||||
return results
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(f, f"{test_set_name}-{key}", results)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
args.lang_dir = Path(args.lang_dir)
|
||||
args.lm_dir = Path(args.lm_dir)
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
assert params.decoding_method in (
|
||||
"ctc-decoding",
|
||||
"1best",
|
||||
"nbest",
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
"nbest-oracle",
|
||||
)
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
if params.causal:
|
||||
assert (
|
||||
"," not in params.chunk_size
|
||||
), "chunk_size should be one value in decoding."
|
||||
assert (
|
||||
"," not in params.left_context_frames
|
||||
), "left_context_frames should be one value in decoding."
|
||||
params.suffix += f"-chunk-{params.chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context_frames}"
|
||||
|
||||
if params.use_averaged_model:
|
||||
params.suffix += "-use-averaged-model"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"Device: {device}")
|
||||
logging.info(params)
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
max_token_id = max(lexicon.tokens)
|
||||
num_classes = max_token_id + 1 # +1 for the blank
|
||||
|
||||
params.vocab_size = num_classes
|
||||
# <blk> and <unk> are defined in local/train_bpe_model.py
|
||||
params.blank_id = 0
|
||||
|
||||
if params.decoding_method == "ctc-decoding":
|
||||
HLG = None
|
||||
H = k2.ctc_topo(
|
||||
max_token=max_token_id,
|
||||
modified=False,
|
||||
device=device,
|
||||
)
|
||||
bpe_model = spm.SentencePieceProcessor()
|
||||
bpe_model.load(str(params.lang_dir / "bpe.model"))
|
||||
else:
|
||||
H = None
|
||||
bpe_model = None
|
||||
HLG = k2.Fsa.from_dict(
|
||||
torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
|
||||
)
|
||||
assert HLG.requires_grad is False
|
||||
|
||||
HLG.scores *= params.hlg_scale
|
||||
if not hasattr(HLG, "lm_scores"):
|
||||
HLG.lm_scores = HLG.scores.clone()
|
||||
|
||||
if params.decoding_method in (
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
):
|
||||
if not (params.lm_dir / "G_4_gram.pt").is_file():
|
||||
logging.info("Loading G_4_gram.fst.txt")
|
||||
logging.warning("It may take 8 minutes.")
|
||||
with open(params.lm_dir / "G_4_gram.fst.txt") as f:
|
||||
first_word_disambig_id = lexicon.word_table["#0"]
|
||||
|
||||
G = k2.Fsa.from_openfst(f.read(), acceptor=False)
|
||||
# G.aux_labels is not needed in later computations, so
|
||||
# remove it here.
|
||||
del G.aux_labels
|
||||
# CAUTION: The following line is crucial.
|
||||
# Arcs entering the back-off state have label equal to #0.
|
||||
# We have to change it to 0 here.
|
||||
G.labels[G.labels >= first_word_disambig_id] = 0
|
||||
# See https://github.com/k2-fsa/k2/issues/874
|
||||
# for why we need to set G.properties to None
|
||||
G.__dict__["_properties"] = None
|
||||
G = k2.Fsa.from_fsas([G]).to(device)
|
||||
G = k2.arc_sort(G)
|
||||
# Save a dummy value so that it can be loaded in C++.
|
||||
# See https://github.com/pytorch/pytorch/issues/67902
|
||||
# for why we need to do this.
|
||||
G.dummy = 1
|
||||
|
||||
torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
|
||||
else:
|
||||
logging.info("Loading pre-compiled G_4_gram.pt")
|
||||
d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
|
||||
if params.decoding_method == "whole-lattice-rescoring":
|
||||
# Add epsilon self-loops to G as we will compose
|
||||
# it with the whole lattice later
|
||||
G = k2.add_epsilon_self_loops(G)
|
||||
G = k2.arc_sort(G)
|
||||
G = G.to(device)
|
||||
|
||||
# G.lm_scores is used to replace HLG.lm_scores during
|
||||
# LM rescoring.
|
||||
G.lm_scores = G.scores.clone()
|
||||
else:
|
||||
G = None
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if i >= 1:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
else:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg + 1
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg + 1:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
filename_start = filenames[-1]
|
||||
filename_end = filenames[0]
|
||||
logging.info(
|
||||
"Calculating the averaged model over iteration checkpoints"
|
||||
f" from {filename_start} (excluded) to {filename_end}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
else:
|
||||
assert params.avg > 0, params.avg
|
||||
start = params.epoch - params.avg
|
||||
assert start >= 1, start
|
||||
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
|
||||
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
|
||||
logging.info(
|
||||
f"Calculating the averaged model over epoch range from "
|
||||
f"{start} (excluded) to {params.epoch}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
test_clean_cuts = librispeech.test_clean_cuts()
|
||||
test_other_cuts = librispeech.test_other_cuts()
|
||||
|
||||
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
|
||||
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
|
||||
|
||||
test_sets = ["test-clean", "test-other"]
|
||||
test_dl = [test_clean_dl, test_other_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dl):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
HLG=HLG,
|
||||
H=H,
|
||||
bpe_model=bpe_model,
|
||||
word_table=lexicon.word_table,
|
||||
G=G,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -116,7 +116,7 @@ from beam_search import (
|
||||
greedy_search_batch,
|
||||
modified_beam_search,
|
||||
)
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_params, get_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
@ -366,15 +366,7 @@ def decode_one_batch(
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
x, x_lens = model.encoder_embed(feature, feature_lens)
|
||||
|
||||
src_key_padding_mask = make_pad_mask(x_lens)
|
||||
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
x, x_lens, src_key_padding_mask
|
||||
)
|
||||
encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
|
||||
|
||||
hyps = []
|
||||
|
||||
@ -694,7 +686,7 @@ def main():
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
|
||||
@ -58,7 +58,6 @@ class Decoder(nn.Module):
|
||||
self.embedding = nn.Embedding(
|
||||
num_embeddings=vocab_size,
|
||||
embedding_dim=decoder_dim,
|
||||
padding_idx=blank_id,
|
||||
)
|
||||
# the balancers are to avoid any drift in the magnitude of the
|
||||
# embeddings, which would interact badly with parameter averaging.
|
||||
|
||||
@ -76,7 +76,7 @@ import torch.nn as nn
|
||||
from decoder import Decoder
|
||||
from onnxruntime.quantization import QuantType, quantize_dynamic
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
from zipformer import Zipformer2
|
||||
|
||||
from icefall.checkpoint import (
|
||||
@ -592,7 +592,7 @@ def main():
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
model.to(device)
|
||||
|
||||
|
||||
@ -73,7 +73,7 @@ import torch.nn as nn
|
||||
from decoder import Decoder
|
||||
from onnxruntime.quantization import QuantType, quantize_dynamic
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
from zipformer import Zipformer2
|
||||
|
||||
from icefall.checkpoint import (
|
||||
@ -440,7 +440,7 @@ def main():
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
model.to(device)
|
||||
|
||||
|
||||
@ -167,7 +167,7 @@ import k2
|
||||
import torch
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from torch import Tensor, nn
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
@ -409,7 +409,7 @@ def main():
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
|
||||
@ -42,7 +42,7 @@ from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
|
||||
|
||||
@ -131,7 +131,7 @@ def main():
|
||||
params.vocab_size = len(symbol_table)
|
||||
|
||||
print("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
|
||||
@ -97,7 +97,7 @@ def read_sound_files(
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0])
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
|
||||
428
egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py
Executable file
428
egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py
Executable file
@ -0,0 +1,428 @@
|
||||
#!/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 loads a checkpoint and uses it to decode waves.
|
||||
You can generate the checkpoint with the following command:
|
||||
|
||||
- For non-streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 1
|
||||
|
||||
- For streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--causal 1 \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 1
|
||||
|
||||
Usage of this script:
|
||||
|
||||
(1) ctc-decoding
|
||||
./zipformer/jit_pretrained_ctc.py \
|
||||
--model-filename ./zipformer/exp/jit_script.pt \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--method ctc-decoding \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(2) 1best
|
||||
./zipformer/jit_pretrained_ctc.py \
|
||||
--model-filename ./zipformer/exp/jit_script.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--method 1best \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(3) nbest-rescoring
|
||||
./zipformer/jit_pretrained_ctc.py \
|
||||
--model-filename ./zipformer/exp/jit_script.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--G data/lm/G_4_gram.pt \
|
||||
--method nbest-rescoring \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(4) whole-lattice-rescoring
|
||||
./zipformer/jit_pretrained_ctc.py \
|
||||
--model-filename ./zipformer/exp/jit_script.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--G data/lm/G_4_gram.pt \
|
||||
--method whole-lattice-rescoring \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from ctc_decode import get_decoding_params
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import get_params
|
||||
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
one_best_decoding,
|
||||
rescore_with_n_best_list,
|
||||
rescore_with_whole_lattice,
|
||||
)
|
||||
from icefall.utils import get_texts
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the torchscript model.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--words-file",
|
||||
type=str,
|
||||
help="""Path to words.txt.
|
||||
Used only when method is not ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--HLG",
|
||||
type=str,
|
||||
help="""Path to HLG.pt.
|
||||
Used only when method is not ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
help="""Path to bpe.model.
|
||||
Used only when method is ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="1best",
|
||||
help="""Decoding method.
|
||||
Possible values are:
|
||||
(0) ctc-decoding - Use CTC decoding. It uses a sentence
|
||||
piece model, i.e., lang_dir/bpe.model, to convert
|
||||
word pieces to words. It needs neither a lexicon
|
||||
nor an n-gram LM.
|
||||
(1) 1best - Use the best path as decoding output. Only
|
||||
the transformer encoder output is used for decoding.
|
||||
We call it HLG decoding.
|
||||
(2) nbest-rescoring. Extract n paths from the decoding lattice,
|
||||
rescore them with an LM, the path with
|
||||
the highest score is the decoding result.
|
||||
We call it HLG decoding + nbest n-gram LM rescoring.
|
||||
(3) whole-lattice-rescoring - Use an LM to rescore the
|
||||
decoding lattice and then use 1best to decode the
|
||||
rescored lattice.
|
||||
We call it HLG decoding + whole-lattice n-gram LM rescoring.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--G",
|
||||
type=str,
|
||||
help="""An LM for rescoring.
|
||||
Used only when method is
|
||||
whole-lattice-rescoring or nbest-rescoring.
|
||||
It's usually a 4-gram LM.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""
|
||||
Used only when method is attention-decoder.
|
||||
It specifies the size of n-best list.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=1.3,
|
||||
help="""
|
||||
Used only when method is whole-lattice-rescoring and nbest-rescoring.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
(Note: You need to tune it on a dataset.)
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""
|
||||
Used only when method is nbest-rescoring.
|
||||
It specifies the scale for lattice.scores when
|
||||
extracting n-best lists. A smaller value results in
|
||||
more unique number of paths with the risk of missing
|
||||
the best path.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float = 16000
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"Expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
logging.info(f"{params}")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
model = torch.jit.load(args.model_filename)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = params.sample_rate
|
||||
opts.mel_opts.num_bins = params.feature_dim
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {params.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(features, feature_lengths)
|
||||
ctc_output = model.ctc_output(encoder_out) # (N, T, C)
|
||||
|
||||
batch_size = ctc_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[
|
||||
[i, 0, feature_lengths[i].item() // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
if params.method == "ctc-decoding":
|
||||
logging.info("Use CTC decoding")
|
||||
max_token_id = params.vocab_size - 1
|
||||
|
||||
H = k2.ctc_topo(
|
||||
max_token=max_token_id,
|
||||
modified=False,
|
||||
device=device,
|
||||
)
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=H,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
token_ids = get_texts(best_path)
|
||||
hyps = sp.decode(token_ids)
|
||||
hyps = [s.split() for s in hyps]
|
||||
elif params.method in [
|
||||
"1best",
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
]:
|
||||
logging.info(f"Loading HLG from {params.HLG}")
|
||||
HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
|
||||
HLG = HLG.to(device)
|
||||
if not hasattr(HLG, "lm_scores"):
|
||||
# For whole-lattice-rescoring and attention-decoder
|
||||
HLG.lm_scores = HLG.scores.clone()
|
||||
|
||||
if params.method in [
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
]:
|
||||
logging.info(f"Loading G from {params.G}")
|
||||
G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
|
||||
G = G.to(device)
|
||||
if params.method == "whole-lattice-rescoring":
|
||||
# Add epsilon self-loops to G as we will compose
|
||||
# it with the whole lattice later
|
||||
G = k2.add_epsilon_self_loops(G)
|
||||
G = k2.arc_sort(G)
|
||||
|
||||
# G.lm_scores is used to replace HLG.lm_scores during
|
||||
# LM rescoring.
|
||||
G.lm_scores = G.scores.clone()
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=HLG,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if params.method == "1best":
|
||||
logging.info("Use HLG decoding")
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
if params.method == "nbest-rescoring":
|
||||
logging.info("Use HLG decoding + LM rescoring")
|
||||
best_path_dict = rescore_with_n_best_list(
|
||||
lattice=lattice,
|
||||
G=G,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
elif params.method == "whole-lattice-rescoring":
|
||||
logging.info("Use HLG decoding + LM rescoring")
|
||||
best_path_dict = rescore_with_whole_lattice(
|
||||
lattice=lattice,
|
||||
G_with_epsilon_loops=G,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
|
||||
hyps = get_texts(best_path)
|
||||
word_sym_table = k2.SymbolTable.from_file(params.words_file)
|
||||
hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
|
||||
else:
|
||||
raise ValueError(f"Unsupported decoding method: {params.method}")
|
||||
|
||||
s = "\n"
|
||||
for filename, hyp in zip(params.sound_files, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
@ -1,4 +1,6 @@
|
||||
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang)
|
||||
# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Wei Kang,
|
||||
# Zengwei Yao)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
@ -14,6 +16,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import k2
|
||||
import torch
|
||||
@ -24,23 +27,25 @@ from icefall.utils import add_sos, make_pad_mask
|
||||
from scaling import ScaledLinear
|
||||
|
||||
|
||||
class Transducer(nn.Module):
|
||||
"""It implements https://arxiv.org/pdf/1211.3711.pdf
|
||||
"Sequence Transduction with Recurrent Neural Networks"
|
||||
"""
|
||||
|
||||
class AsrModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
encoder_embed: nn.Module,
|
||||
encoder: EncoderInterface,
|
||||
decoder: nn.Module,
|
||||
joiner: nn.Module,
|
||||
encoder_dim: int,
|
||||
decoder_dim: int,
|
||||
joiner_dim: int,
|
||||
vocab_size: int,
|
||||
decoder: Optional[nn.Module] = None,
|
||||
joiner: Optional[nn.Module] = None,
|
||||
encoder_dim: int = 384,
|
||||
decoder_dim: int = 512,
|
||||
vocab_size: int = 500,
|
||||
use_transducer: bool = True,
|
||||
use_ctc: bool = False,
|
||||
):
|
||||
"""
|
||||
"""A joint CTC & Transducer ASR model.
|
||||
|
||||
- Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (http://imagine.enpc.fr/~obozinsg/teaching/mva_gm/papers/ctc.pdf)
|
||||
- Sequence Transduction with Recurrent Neural Networks (https://arxiv.org/pdf/1211.3711.pdf)
|
||||
- Pruned RNN-T for fast, memory-efficient ASR training (https://arxiv.org/pdf/2206.13236.pdf)
|
||||
|
||||
Args:
|
||||
encoder_embed:
|
||||
It is a Convolutional 2D subsampling module. It converts
|
||||
@ -55,47 +60,133 @@ class Transducer(nn.Module):
|
||||
It is the prediction network in the paper. Its input shape
|
||||
is (N, U) and its output shape is (N, U, decoder_dim).
|
||||
It should contain one attribute: `blank_id`.
|
||||
It is used when use_transducer is True.
|
||||
joiner:
|
||||
It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim).
|
||||
Its output shape is (N, T, U, vocab_size). Note that its output contains
|
||||
unnormalized probs, i.e., not processed by log-softmax.
|
||||
It is used when use_transducer is True.
|
||||
use_transducer:
|
||||
Whether use transducer head. Default: True.
|
||||
use_ctc:
|
||||
Whether use CTC head. Default: False.
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
assert (
|
||||
use_transducer or use_ctc
|
||||
), f"At least one of them should be True, but got use_transducer={use_transducer}, use_ctc={use_ctc}"
|
||||
|
||||
assert isinstance(encoder, EncoderInterface), type(encoder)
|
||||
assert hasattr(decoder, "blank_id")
|
||||
|
||||
self.encoder_embed = encoder_embed
|
||||
self.encoder = encoder
|
||||
self.decoder = decoder
|
||||
self.joiner = joiner
|
||||
|
||||
self.simple_am_proj = ScaledLinear(
|
||||
encoder_dim,
|
||||
vocab_size,
|
||||
initial_scale=0.25,
|
||||
)
|
||||
self.simple_lm_proj = ScaledLinear(
|
||||
decoder_dim,
|
||||
vocab_size,
|
||||
initial_scale=0.25,
|
||||
)
|
||||
self.use_transducer = use_transducer
|
||||
if use_transducer:
|
||||
# Modules for Transducer head
|
||||
assert decoder is not None
|
||||
assert hasattr(decoder, "blank_id")
|
||||
assert joiner is not None
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lens: torch.Tensor,
|
||||
y: k2.RaggedTensor,
|
||||
prune_range: int = 5,
|
||||
am_scale: float = 0.0,
|
||||
lm_scale: float = 0.0,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
self.decoder = decoder
|
||||
self.joiner = joiner
|
||||
|
||||
self.simple_am_proj = ScaledLinear(
|
||||
encoder_dim, vocab_size, initial_scale=0.25
|
||||
)
|
||||
self.simple_lm_proj = ScaledLinear(
|
||||
decoder_dim, vocab_size, initial_scale=0.25
|
||||
)
|
||||
else:
|
||||
assert decoder is None
|
||||
assert joiner is None
|
||||
|
||||
self.use_ctc = use_ctc
|
||||
if use_ctc:
|
||||
# Modules for CTC head
|
||||
self.ctc_output = nn.Sequential(
|
||||
nn.Dropout(p=0.1),
|
||||
nn.Linear(encoder_dim, vocab_size),
|
||||
nn.LogSoftmax(dim=-1),
|
||||
)
|
||||
|
||||
def forward_encoder(
|
||||
self, x: torch.Tensor, x_lens: torch.Tensor
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute encoder outputs.
|
||||
Args:
|
||||
x:
|
||||
A 3-D tensor of shape (N, T, C).
|
||||
x_lens:
|
||||
A 1-D tensor of shape (N,). It contains the number of frames in `x`
|
||||
before padding.
|
||||
|
||||
Returns:
|
||||
encoder_out:
|
||||
Encoder output, of shape (N, T, C).
|
||||
encoder_out_lens:
|
||||
Encoder output lengths, of shape (N,).
|
||||
"""
|
||||
# logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
|
||||
x, x_lens = self.encoder_embed(x, x_lens)
|
||||
# logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")
|
||||
|
||||
src_key_padding_mask = make_pad_mask(x_lens)
|
||||
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
|
||||
|
||||
encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
|
||||
|
||||
encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens)
|
||||
|
||||
return encoder_out, encoder_out_lens
|
||||
|
||||
def forward_ctc(
|
||||
self,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
targets: torch.Tensor,
|
||||
target_lengths: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
"""Compute CTC loss.
|
||||
Args:
|
||||
encoder_out:
|
||||
Encoder output, of shape (N, T, C).
|
||||
encoder_out_lens:
|
||||
Encoder output lengths, of shape (N,).
|
||||
targets:
|
||||
Target Tensor of shape (sum(target_lengths)). The targets are assumed
|
||||
to be un-padded and concatenated within 1 dimension.
|
||||
"""
|
||||
# Compute CTC log-prob
|
||||
ctc_output = self.ctc_output(encoder_out) # (N, T, C)
|
||||
|
||||
ctc_loss = torch.nn.functional.ctc_loss(
|
||||
log_probs=ctc_output.permute(1, 0, 2), # (T, N, C)
|
||||
targets=targets,
|
||||
input_lengths=encoder_out_lens,
|
||||
target_lengths=target_lengths,
|
||||
reduction="sum",
|
||||
)
|
||||
return ctc_loss
|
||||
|
||||
def forward_transducer(
|
||||
self,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
y: k2.RaggedTensor,
|
||||
y_lens: torch.Tensor,
|
||||
prune_range: int = 5,
|
||||
am_scale: float = 0.0,
|
||||
lm_scale: float = 0.0,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""Compute Transducer loss.
|
||||
Args:
|
||||
encoder_out:
|
||||
Encoder output, of shape (N, T, C).
|
||||
encoder_out_lens:
|
||||
Encoder output lengths, of shape (N,).
|
||||
y:
|
||||
A ragged tensor with 2 axes [utt][label]. It contains labels of each
|
||||
utterance.
|
||||
@ -108,37 +199,8 @@ class Transducer(nn.Module):
|
||||
lm_scale:
|
||||
The scale to smooth the loss with lm (output of predictor network)
|
||||
part
|
||||
Returns:
|
||||
Return the transducer loss.
|
||||
|
||||
Note:
|
||||
Regarding am_scale & lm_scale, it will make the loss-function one of
|
||||
the form:
|
||||
lm_scale * lm_probs + am_scale * am_probs +
|
||||
(1-lm_scale-am_scale) * combined_probs
|
||||
"""
|
||||
assert x.ndim == 3, x.shape
|
||||
assert x_lens.ndim == 1, x_lens.shape
|
||||
assert y.num_axes == 2, y.num_axes
|
||||
|
||||
assert x.size(0) == x_lens.size(0) == y.dim0
|
||||
|
||||
# logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
|
||||
x, x_lens = self.encoder_embed(x, x_lens)
|
||||
# logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")
|
||||
|
||||
src_key_padding_mask = make_pad_mask(x_lens)
|
||||
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
|
||||
|
||||
encoder_out, x_lens = self.encoder(x, x_lens, src_key_padding_mask)
|
||||
encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
|
||||
assert torch.all(x_lens > 0)
|
||||
|
||||
# Now for the decoder, i.e., the prediction network
|
||||
row_splits = y.shape.row_splits(1)
|
||||
y_lens = row_splits[1:] - row_splits[:-1]
|
||||
|
||||
blank_id = self.decoder.blank_id
|
||||
sos_y = add_sos(y, sos_id=blank_id)
|
||||
|
||||
@ -159,7 +221,7 @@ class Transducer(nn.Module):
|
||||
device=encoder_out.device,
|
||||
)
|
||||
boundary[:, 2] = y_lens
|
||||
boundary[:, 3] = x_lens
|
||||
boundary[:, 3] = encoder_out_lens
|
||||
|
||||
lm = self.simple_lm_proj(decoder_out)
|
||||
am = self.simple_am_proj(encoder_out)
|
||||
@ -214,4 +276,83 @@ class Transducer(nn.Module):
|
||||
reduction="sum",
|
||||
)
|
||||
|
||||
return (simple_loss, pruned_loss)
|
||||
return simple_loss, pruned_loss
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lens: torch.Tensor,
|
||||
y: k2.RaggedTensor,
|
||||
prune_range: int = 5,
|
||||
am_scale: float = 0.0,
|
||||
lm_scale: float = 0.0,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Args:
|
||||
x:
|
||||
A 3-D tensor of shape (N, T, C).
|
||||
x_lens:
|
||||
A 1-D tensor of shape (N,). It contains the number of frames in `x`
|
||||
before padding.
|
||||
y:
|
||||
A ragged tensor with 2 axes [utt][label]. It contains labels of each
|
||||
utterance.
|
||||
prune_range:
|
||||
The prune range for rnnt loss, it means how many symbols(context)
|
||||
we are considering for each frame to compute the loss.
|
||||
am_scale:
|
||||
The scale to smooth the loss with am (output of encoder network)
|
||||
part
|
||||
lm_scale:
|
||||
The scale to smooth the loss with lm (output of predictor network)
|
||||
part
|
||||
Returns:
|
||||
Return the transducer losses and CTC loss,
|
||||
in form of (simple_loss, pruned_loss, ctc_loss)
|
||||
|
||||
Note:
|
||||
Regarding am_scale & lm_scale, it will make the loss-function one of
|
||||
the form:
|
||||
lm_scale * lm_probs + am_scale * am_probs +
|
||||
(1-lm_scale-am_scale) * combined_probs
|
||||
"""
|
||||
assert x.ndim == 3, x.shape
|
||||
assert x_lens.ndim == 1, x_lens.shape
|
||||
assert y.num_axes == 2, y.num_axes
|
||||
|
||||
assert x.size(0) == x_lens.size(0) == y.dim0
|
||||
|
||||
# Compute encoder outputs
|
||||
encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens)
|
||||
|
||||
row_splits = y.shape.row_splits(1)
|
||||
y_lens = row_splits[1:] - row_splits[:-1]
|
||||
|
||||
if self.use_transducer:
|
||||
# Compute transducer loss
|
||||
simple_loss, pruned_loss = self.forward_transducer(
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
y=y.to(x.device),
|
||||
y_lens=y_lens,
|
||||
prune_range=prune_range,
|
||||
am_scale=am_scale,
|
||||
lm_scale=lm_scale,
|
||||
)
|
||||
else:
|
||||
simple_loss = torch.empty(0)
|
||||
pruned_loss = torch.empty(0)
|
||||
|
||||
if self.use_ctc:
|
||||
# Compute CTC loss
|
||||
targets = y.values
|
||||
ctc_loss = self.forward_ctc(
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
targets=targets,
|
||||
target_lengths=y_lens,
|
||||
)
|
||||
else:
|
||||
ctc_loss = torch.empty(0)
|
||||
|
||||
return simple_loss, pruned_loss, ctc_loss
|
||||
|
||||
@ -123,7 +123,7 @@ from beam_search import (
|
||||
modified_beam_search,
|
||||
)
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall.utils import make_pad_mask
|
||||
|
||||
@ -249,7 +249,7 @@ def read_sound_files(
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0])
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
@ -285,7 +285,7 @@ def main():
|
||||
), "left_context_frames should be one value in decoding."
|
||||
|
||||
logging.info("Creating model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
@ -319,13 +319,7 @@ def main():
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
# model forward
|
||||
x, x_lens = model.encoder_embed(features, feature_lengths)
|
||||
|
||||
src_key_padding_mask = make_pad_mask(x_lens)
|
||||
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask)
|
||||
encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)
|
||||
|
||||
hyps = []
|
||||
msg = f"Using {params.method}"
|
||||
|
||||
446
egs/librispeech/ASR/zipformer/pretrained_ctc.py
Executable file
446
egs/librispeech/ASR/zipformer/pretrained_ctc.py
Executable file
@ -0,0 +1,446 @@
|
||||
#!/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 loads a checkpoint and uses it to decode waves.
|
||||
You can generate the checkpoint with the following command:
|
||||
|
||||
- For non-streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9
|
||||
|
||||
- For streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp \
|
||||
--use-ctc 1 \
|
||||
--causal 1 \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9
|
||||
|
||||
Usage of this script:
|
||||
|
||||
(1) ctc-decoding
|
||||
./zipformer/pretrained_ctc.py \
|
||||
--checkpoint ./zipformer/exp/pretrained.pt \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--method ctc-decoding \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(2) 1best
|
||||
./zipformer/pretrained_ctc.py \
|
||||
--checkpoint ./zipformer/exp/pretrained.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--method 1best \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(3) nbest-rescoring
|
||||
./zipformer/pretrained_ctc.py \
|
||||
--checkpoint ./zipformer/exp/pretrained.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--G data/lm/G_4_gram.pt \
|
||||
--method nbest-rescoring \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
|
||||
(4) whole-lattice-rescoring
|
||||
./zipformer/pretrained_ctc.py \
|
||||
--checkpoint ./zipformer/exp/pretrained.pt \
|
||||
--HLG data/lang_bpe_500/HLG.pt \
|
||||
--words-file data/lang_bpe_500/words.txt \
|
||||
--G data/lm/G_4_gram.pt \
|
||||
--method whole-lattice-rescoring \
|
||||
--sample-rate 16000 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from ctc_decode import get_decoding_params
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_model
|
||||
|
||||
from icefall.decode import (
|
||||
get_lattice,
|
||||
one_best_decoding,
|
||||
rescore_with_n_best_list,
|
||||
rescore_with_whole_lattice,
|
||||
)
|
||||
from icefall.utils import get_texts
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--checkpoint",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the checkpoint. "
|
||||
"The checkpoint is assumed to be saved by "
|
||||
"icefall.checkpoint.save_checkpoint().",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--context-size",
|
||||
type=int,
|
||||
default=2,
|
||||
help="The context size in the decoder. 1 means bigram; " "2 means tri-gram",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--words-file",
|
||||
type=str,
|
||||
help="""Path to words.txt.
|
||||
Used only when method is not ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--HLG",
|
||||
type=str,
|
||||
help="""Path to HLG.pt.
|
||||
Used only when method is not ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
help="""Path to bpe.model.
|
||||
Used only when method is ctc-decoding.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="1best",
|
||||
help="""Decoding method.
|
||||
Possible values are:
|
||||
(0) ctc-decoding - Use CTC decoding. It uses a sentence
|
||||
piece model, i.e., lang_dir/bpe.model, to convert
|
||||
word pieces to words. It needs neither a lexicon
|
||||
nor an n-gram LM.
|
||||
(1) 1best - Use the best path as decoding output. Only
|
||||
the transformer encoder output is used for decoding.
|
||||
We call it HLG decoding.
|
||||
(2) nbest-rescoring. Extract n paths from the decoding lattice,
|
||||
rescore them with an LM, the path with
|
||||
the highest score is the decoding result.
|
||||
We call it HLG decoding + nbest n-gram LM rescoring.
|
||||
(3) whole-lattice-rescoring - Use an LM to rescore the
|
||||
decoding lattice and then use 1best to decode the
|
||||
rescored lattice.
|
||||
We call it HLG decoding + whole-lattice n-gram LM rescoring.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--G",
|
||||
type=str,
|
||||
help="""An LM for rescoring.
|
||||
Used only when method is
|
||||
whole-lattice-rescoring or nbest-rescoring.
|
||||
It's usually a 4-gram LM.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=100,
|
||||
help="""
|
||||
Used only when method is attention-decoder.
|
||||
It specifies the size of n-best list.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=1.3,
|
||||
help="""
|
||||
Used only when method is whole-lattice-rescoring and nbest-rescoring.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
(Note: You need to tune it on a dataset.)
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="""
|
||||
Used only when method is nbest-rescoring.
|
||||
It specifies the scale for lattice.scores when
|
||||
extracting n-best lists. A smaller value results in
|
||||
more unique number of paths with the risk of missing
|
||||
the best path.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float = 16000
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert sample_rate == expected_sample_rate, (
|
||||
f"expected sample rate: {expected_sample_rate}. " f"Given: {sample_rate}"
|
||||
)
|
||||
# We use only the first channel
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
# add decoding params
|
||||
params.update(get_decoding_params())
|
||||
params.update(vars(args))
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
params.blank_id = 0
|
||||
|
||||
logging.info(f"{params}")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
logging.info("Creating model")
|
||||
model = get_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
checkpoint = torch.load(args.checkpoint, map_location="cpu")
|
||||
model.load_state_dict(checkpoint["model"], strict=False)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = params.sample_rate
|
||||
opts.mel_opts.num_bins = params.feature_dim
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {params.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)
|
||||
ctc_output = model.ctc_output(encoder_out) # (N, T, C)
|
||||
|
||||
batch_size = ctc_output.shape[0]
|
||||
supervision_segments = torch.tensor(
|
||||
[
|
||||
[i, 0, feature_lengths[i].item() // params.subsampling_factor]
|
||||
for i in range(batch_size)
|
||||
],
|
||||
dtype=torch.int32,
|
||||
)
|
||||
|
||||
if params.method == "ctc-decoding":
|
||||
logging.info("Use CTC decoding")
|
||||
max_token_id = params.vocab_size - 1
|
||||
|
||||
H = k2.ctc_topo(
|
||||
max_token=max_token_id,
|
||||
modified=False,
|
||||
device=device,
|
||||
)
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=H,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
token_ids = get_texts(best_path)
|
||||
hyps = sp.decode(token_ids)
|
||||
hyps = [s.split() for s in hyps]
|
||||
elif params.method in [
|
||||
"1best",
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
]:
|
||||
logging.info(f"Loading HLG from {params.HLG}")
|
||||
HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
|
||||
HLG = HLG.to(device)
|
||||
if not hasattr(HLG, "lm_scores"):
|
||||
# For whole-lattice-rescoring and attention-decoder
|
||||
HLG.lm_scores = HLG.scores.clone()
|
||||
|
||||
if params.method in [
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
]:
|
||||
logging.info(f"Loading G from {params.G}")
|
||||
G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
|
||||
G = G.to(device)
|
||||
if params.method == "whole-lattice-rescoring":
|
||||
# Add epsilon self-loops to G as we will compose
|
||||
# it with the whole lattice later
|
||||
G = k2.add_epsilon_self_loops(G)
|
||||
G = k2.arc_sort(G)
|
||||
|
||||
# G.lm_scores is used to replace HLG.lm_scores during
|
||||
# LM rescoring.
|
||||
G.lm_scores = G.scores.clone()
|
||||
|
||||
lattice = get_lattice(
|
||||
nnet_output=ctc_output,
|
||||
decoding_graph=HLG,
|
||||
supervision_segments=supervision_segments,
|
||||
search_beam=params.search_beam,
|
||||
output_beam=params.output_beam,
|
||||
min_active_states=params.min_active_states,
|
||||
max_active_states=params.max_active_states,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
)
|
||||
|
||||
if params.method == "1best":
|
||||
logging.info("Use HLG decoding")
|
||||
best_path = one_best_decoding(
|
||||
lattice=lattice, use_double_scores=params.use_double_scores
|
||||
)
|
||||
if params.method == "nbest-rescoring":
|
||||
logging.info("Use HLG decoding + LM rescoring")
|
||||
best_path_dict = rescore_with_n_best_list(
|
||||
lattice=lattice,
|
||||
G=G,
|
||||
num_paths=params.num_paths,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
nbest_scale=params.nbest_scale,
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
elif params.method == "whole-lattice-rescoring":
|
||||
logging.info("Use HLG decoding + LM rescoring")
|
||||
best_path_dict = rescore_with_whole_lattice(
|
||||
lattice=lattice,
|
||||
G_with_epsilon_loops=G,
|
||||
lm_scale_list=[params.ngram_lm_scale],
|
||||
)
|
||||
best_path = next(iter(best_path_dict.values()))
|
||||
|
||||
hyps = get_texts(best_path)
|
||||
word_sym_table = k2.SymbolTable.from_file(params.words_file)
|
||||
hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
|
||||
else:
|
||||
raise ValueError(f"Unsupported decoding method: {params.method}")
|
||||
|
||||
s = "\n"
|
||||
for filename, hyp in zip(params.sound_files, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
@ -51,7 +51,7 @@ from streaming_beam_search import (
|
||||
)
|
||||
from torch import Tensor, nn
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
from train import add_model_arguments, get_params, get_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
@ -756,7 +756,7 @@ def main():
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
|
||||
@ -44,6 +44,10 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
|
||||
--full-libri 1 \
|
||||
--max-duration 1000
|
||||
|
||||
It supports training with:
|
||||
- transducer loss (default), with `--use-transducer True --use-ctc False`
|
||||
- ctc loss (not recommended), with `--use-transducer False --use-ctc True`
|
||||
- transducer loss & ctc loss, with `--use-transducer True --use-ctc True`
|
||||
"""
|
||||
|
||||
|
||||
@ -67,7 +71,7 @@ from joiner import Joiner
|
||||
from lhotse.cut import Cut
|
||||
from lhotse.dataset.sampling.base import CutSampler
|
||||
from lhotse.utils import fix_random_seed
|
||||
from model import Transducer
|
||||
from model import AsrModel
|
||||
from optim import Eden, ScaledAdam
|
||||
from scaling import ScheduledFloat
|
||||
from subsampling import Conv2dSubsampling
|
||||
@ -240,6 +244,20 @@ def add_model_arguments(parser: argparse.ArgumentParser):
|
||||
"chunk left-context frames will be chosen randomly from this list; else not relevant.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-transducer",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="If True, use Transducer head.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-ctc",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="If True, use CTC head.",
|
||||
)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
@ -378,6 +396,13 @@ def get_parser():
|
||||
"with this parameter before adding to the final loss.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ctc-loss-scale",
|
||||
type=float,
|
||||
default=0.2,
|
||||
help="Scale for CTC loss.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
@ -578,21 +603,33 @@ def get_joiner_model(params: AttributeDict) -> nn.Module:
|
||||
return joiner
|
||||
|
||||
|
||||
def get_transducer_model(params: AttributeDict) -> nn.Module:
|
||||
def get_model(params: AttributeDict) -> nn.Module:
|
||||
assert (
|
||||
params.use_transducer or params.use_ctc
|
||||
), (f"At least one of them should be True, "
|
||||
f"but got params.use_transducer={params.use_transducer}, "
|
||||
f"params.use_ctc={params.use_ctc}")
|
||||
|
||||
encoder_embed = get_encoder_embed(params)
|
||||
encoder = get_encoder_model(params)
|
||||
decoder = get_decoder_model(params)
|
||||
joiner = get_joiner_model(params)
|
||||
|
||||
model = Transducer(
|
||||
if params.use_transducer:
|
||||
decoder = get_decoder_model(params)
|
||||
joiner = get_joiner_model(params)
|
||||
else:
|
||||
decoder = None
|
||||
joiner = None
|
||||
|
||||
model = AsrModel(
|
||||
encoder_embed=encoder_embed,
|
||||
encoder=encoder,
|
||||
decoder=decoder,
|
||||
joiner=joiner,
|
||||
encoder_dim=int(max(params.encoder_dim.split(","))),
|
||||
encoder_dim=max(_to_int_tuple(params.encoder_dim)),
|
||||
decoder_dim=params.decoder_dim,
|
||||
joiner_dim=params.joiner_dim,
|
||||
vocab_size=params.vocab_size,
|
||||
use_transducer=params.use_transducer,
|
||||
use_ctc=params.use_ctc,
|
||||
)
|
||||
return model
|
||||
|
||||
@ -721,7 +758,7 @@ def compute_loss(
|
||||
is_training: bool,
|
||||
) -> Tuple[Tensor, MetricsTracker]:
|
||||
"""
|
||||
Compute CTC loss given the model and its inputs.
|
||||
Compute loss given the model and its inputs.
|
||||
|
||||
Args:
|
||||
params:
|
||||
@ -752,10 +789,10 @@ def compute_loss(
|
||||
|
||||
texts = batch["supervisions"]["text"]
|
||||
y = sp.encode(texts, out_type=int)
|
||||
y = k2.RaggedTensor(y).to(device)
|
||||
y = k2.RaggedTensor(y)
|
||||
|
||||
with torch.set_grad_enabled(is_training):
|
||||
simple_loss, pruned_loss = model(
|
||||
simple_loss, pruned_loss, ctc_loss = model(
|
||||
x=feature,
|
||||
x_lens=feature_lens,
|
||||
y=y,
|
||||
@ -764,21 +801,27 @@ def compute_loss(
|
||||
lm_scale=params.lm_scale,
|
||||
)
|
||||
|
||||
s = params.simple_loss_scale
|
||||
# take down the scale on the simple loss from 1.0 at the start
|
||||
# to params.simple_loss scale by warm_step.
|
||||
simple_loss_scale = (
|
||||
s
|
||||
if batch_idx_train >= warm_step
|
||||
else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
|
||||
)
|
||||
pruned_loss_scale = (
|
||||
1.0
|
||||
if batch_idx_train >= warm_step
|
||||
else 0.1 + 0.9 * (batch_idx_train / warm_step)
|
||||
)
|
||||
loss = 0.0
|
||||
|
||||
loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
|
||||
if params.use_transducer:
|
||||
s = params.simple_loss_scale
|
||||
# take down the scale on the simple loss from 1.0 at the start
|
||||
# to params.simple_loss scale by warm_step.
|
||||
simple_loss_scale = (
|
||||
s if batch_idx_train >= warm_step
|
||||
else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
|
||||
)
|
||||
pruned_loss_scale = (
|
||||
1.0 if batch_idx_train >= warm_step
|
||||
else 0.1 + 0.9 * (batch_idx_train / warm_step)
|
||||
)
|
||||
loss += (
|
||||
simple_loss_scale * simple_loss
|
||||
+ pruned_loss_scale * pruned_loss
|
||||
)
|
||||
|
||||
if params.use_ctc:
|
||||
loss += params.ctc_loss_scale * ctc_loss
|
||||
|
||||
assert loss.requires_grad == is_training
|
||||
|
||||
@ -789,8 +832,11 @@ def compute_loss(
|
||||
|
||||
# Note: We use reduction=sum while computing the loss.
|
||||
info["loss"] = loss.detach().cpu().item()
|
||||
info["simple_loss"] = simple_loss.detach().cpu().item()
|
||||
info["pruned_loss"] = pruned_loss.detach().cpu().item()
|
||||
if params.use_transducer:
|
||||
info["simple_loss"] = simple_loss.detach().cpu().item()
|
||||
info["pruned_loss"] = pruned_loss.detach().cpu().item()
|
||||
if params.use_ctc:
|
||||
info["ctc_loss"] = ctc_loss.detach().cpu().item()
|
||||
|
||||
return loss, info
|
||||
|
||||
@ -1071,10 +1117,13 @@ def run(rank, world_size, args):
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
if not params.use_transducer:
|
||||
params.ctc_loss_scale = 1.0
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
model = get_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
@ -2190,7 +2190,7 @@ class ConvolutionModule(nn.Module):
|
||||
|
||||
x = self.in_proj(x) # (time, batch, 2*channels)
|
||||
|
||||
x, s = x.chunk(2, dim=-1)
|
||||
x, s = x.chunk(2, dim=2)
|
||||
s = self.sigmoid(s)
|
||||
x = x * s
|
||||
# (time, batch, channels)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user