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[WIP] Pruned-transducer-stateless5-for-WenetSpeech (offline and streaming) (#447)
* pruned-rnnt5-for-wenetspeech * style check * style check * add streaming conformer * add streaming decode * changes codes for fast_beam_search and export cpu jit * add modified-beam-search for streaming decoding * add modified-beam-search for streaming decoding * change for streaming_beam_search.py * add README.md and RESULTS.md * change for style_check.yml * do some changes * do some changes for export.py * add some decode commands for usage * add streaming results on README.md
This commit is contained in:
Mingshuang Luo
GitHub
parent
385645d533
commit
f26b62ac00
2
.github/workflows/style_check.yml
vendored
2
.github/workflows/style_check.yml
vendored
@ -29,7 +29,7 @@ jobs:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-18.04, macos-10.15]
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os: [ubuntu-18.04, macos-latest]
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python-version: [3.7, 3.9]
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fail-fast: false
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15
README.md
15
README.md
@ -250,9 +250,9 @@ We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless mod
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### WenetSpeech
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We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless2].
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We provide some models for this recipe: [Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless2] and [Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless5].
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#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset)
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#### Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset, offline ASR)
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| | Dev | Test-Net | Test-Meeting |
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|----------------------|-------|----------|--------------|
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@ -260,7 +260,15 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
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| fast beam search | 7.94 | 8.74 | 13.80 |
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| modified beam search | 7.76 | 8.71 | 13.41 |
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
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#### Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset)
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**Streaming**:
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| | Dev | Test-Net | Test-Meeting |
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|----------------------|-------|----------|--------------|
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| greedy_search | 8.78 | 10.12 | 16.16 |
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| modified_beam_search | 8.53| 9.95 | 15.81 |
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| fast_beam_search| 9.01 | 10.47 | 16.28 |
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We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless2 model: [](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
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### Alimeeting
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@ -333,6 +341,7 @@ Please see: [ | Embedding + Conv1d | Using k2 pruned RNN-T loss | |
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| `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss | |
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The decoder in `transducer_stateless` is modified from the paper
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[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
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@ -1,12 +1,84 @@
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## Results
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### WenetSpeech char-based training results (offline and streaming) (Pruned Transducer 5)
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#### 2022-07-22
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/447.
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When training with the L subset, the CERs are
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**Offline**:
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|decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING|
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|-- | -- | -- | -- | -- | -- | --|
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|greedy_search | 4 | 1 | True | 8.22 | 9.03 | 14.54|
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|modified_beam_search | 4 | 1 | True | **8.17** | **9.04** | **14.44**|
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|fast_beam_search | 4 | 1 | True | 8.29 | 9.00 | 14.93|
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The offline training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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./pruned_transducer_stateless5/train.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_offline \
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--world-size 8 \
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--num-epochs 15 \
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--start-epoch 2 \
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--max-duration 120 \
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--valid-interval 3000 \
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--model-warm-step 3000 \
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--save-every-n 8000 \
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--average-period 1000 \
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--training-subset L
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```
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The tensorboard training log can be found at https://tensorboard.dev/experiment/SvnN2jfyTB2Hjqu22Z7ZoQ/#scalars .
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A pre-trained offline model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline>
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**Streaming**:
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|decoding-method| epoch | avg | use-averaged-model | DEV | TEST-NET | TEST-MEETING|
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|--|--|--|--|--|--|--|
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| greedy_search | 7| 1| True | 8.78 | 10.12 | 16.16 |
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| modified_beam_search | 7| 1| True| **8.53**| **9.95** | **15.81** |
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| fast_beam_search | 7 | 1| True | 9.01 | 10.47 | 16.28 |
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The streaming training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
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./pruned_transducer_stateless5/train.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_streaming \
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--world-size 8 \
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--num-epochs 15 \
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--start-epoch 1 \
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--max-duration 140 \
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--valid-interval 3000 \
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--model-warm-step 3000 \
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--save-every-n 8000 \
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--average-period 1000 \
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--training-subset L \
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--dynamic-chunk-training True \
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--causal-convolution True \
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--short-chunk-size 25 \
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--num-left-chunks 4
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```
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The tensorboard training log can be found at https://tensorboard.dev/experiment/E2NXPVflSOKWepzJ1a1uDQ/#scalars .
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A pre-trained offline model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_streaming>
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### WenetSpeech char-based training results (Pruned Transducer 2)
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#### 2022-05-19
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/349.
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When training with the L subset, the WERs are
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When training with the L subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|-------|----------|--------------|------------------------------------------|
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@ -72,7 +144,7 @@ avg=2
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--max-states 8
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```
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When training with the M subset, the WERs are
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When training with the M subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|--------|-----------|---------------|-------------------------------------------|
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@ -81,7 +153,7 @@ When training with the M subset, the WERs are
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| fast beam search (set as default) | 10.18 | 11.10 | 19.32 | --epoch 29, --avg 11, --max-duration 1500 |
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When training with the S subset, the WERs are
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When training with the S subset, the CERs are
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| | dev | test-net | test-meeting | comment |
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|------------------------------------|--------|-----------|---------------|-------------------------------------------|
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@ -348,7 +348,6 @@ def get_params() -> AttributeDict:
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epochs.
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- log_interval: Print training loss if batch_idx % log_interval` is 0
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- reset_interval: Reset statistics if batch_idx % reset_interval is 0
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- valid_interval: Run validation if batch_idx % valid_interval is 0
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- feature_dim: The model input dim. It has to match the one used
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in computing features.
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- subsampling_factor: The subsampling factor for the model.
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@ -376,7 +375,6 @@ def get_params() -> AttributeDict:
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"decoder_dim": 512,
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# parameters for joiner
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"joiner_dim": 512,
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# parameters for Noam
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"env_info": get_env_info(),
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}
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)
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@ -0,0 +1 @@
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../pruned_transducer_stateless2/asr_datamodule.py
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1
egs/wenetspeech/ASR/pruned_transducer_stateless5/beam_search.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/beam_search.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/pruned_transducer_stateless5/beam_search.py
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1555
egs/wenetspeech/ASR/pruned_transducer_stateless5/conformer.py
Normal file
1555
egs/wenetspeech/ASR/pruned_transducer_stateless5/conformer.py
Normal file
File diff suppressed because it is too large
Load Diff
778
egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py
Executable file
778
egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py
Executable file
@ -0,0 +1,778 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
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# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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When training with the L subset, the offline usage:
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(1) greedy search
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./pruned_transducer_stateless5/decode.py \
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--epoch 4 \
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--avg 1 \
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--exp-dir ./pruned_transducer_stateless5/exp_L_offline \
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--lang-dir data/lang_char \
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--max-duration 100 \
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--decoding-method greedy_search
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(2) modified beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch 4 \
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--avg 1 \
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--exp-dir ./pruned_transducer_stateless5/exp_L_offline \
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--lang-dir data/lang_char \
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--max-duration 100 \
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--decoding-method modified_beam_search \
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--beam-size 4
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(3) fast beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch 4 \
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--avg 1 \
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--exp-dir ./pruned_transducer_stateless5/exp_L_offline \
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--lang-dir data/lang_char \
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--max-duration 1500 \
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--decoding-method fast_beam_search \
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--beam 4 \
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--max-contexts 4 \
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--max-states 8
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When training with the L subset, the streaming usage:
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(1) greedy search
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./pruned_transducer_stateless5/decode.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_streaming \
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--use-averaged-model True \
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--max-duration 600 \
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--epoch 7 \
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--avg 1 \
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--decoding-method greedy_search \
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--simulate-streaming 1 \
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--causal-convolution 1 \
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--decode-chunk-size 16 \
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--left-context 64
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(2) modified beam search
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./pruned_transducer_stateless5/decode.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_streaming \
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--use-averaged-model True \
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--max-duration 600 \
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--epoch 7 \
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--avg 1 \
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--decoding-method modified_beam_search \
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--simulate-streaming 1 \
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--causal-convolution 1 \
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--decode-chunk-size 16 \
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--left-context 64
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(3) fast beam search
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./pruned_transducer_stateless5/decode.py \
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--lang-dir data/lang_char \
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--exp-dir pruned_transducer_stateless5/exp_L_streaming \
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--use-averaged-model True \
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--max-duration 600 \
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--epoch 7 \
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--avg 1 \
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--decoding-method fast_beam_search \
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--simulate-streaming 1 \
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--causal-convolution 1 \
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--decode-chunk-size 16 \
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--left-context 64
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"""
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import argparse
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import logging
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import math
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import k2
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import torch
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import torch.nn as nn
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from asr_datamodule import WenetSpeechAsrDataModule
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from beam_search import (
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beam_search,
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fast_beam_search_one_best,
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greedy_search,
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greedy_search_batch,
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modified_beam_search,
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)
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from train import add_model_arguments, get_params, get_transducer_model
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.lexicon import Lexicon
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from icefall.utils import (
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AttributeDict,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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LOG_EPS = math.log(1e-10)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=30,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 1.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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""",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=15,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch' and '--iter'",
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)
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parser.add_argument(
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"--use-averaged-model",
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type=str2bool,
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default=True,
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help="Whether to load averaged model. Currently it only supports "
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"using --epoch. If True, it would decode with the averaged model "
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"over the epoch range from `epoch-avg` (excluded) to `epoch`."
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"Actually only the models with epoch number of `epoch-avg` and "
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"`epoch` are loaded for averaging. ",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="pruned_transducer_stateless5/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--lang-dir",
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type=str,
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default="data/lang_char",
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help="""The lang dir
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It contains language related input files such as
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"lexicon.txt"
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""",
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)
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parser.add_argument(
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="""Possible values are:
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- greedy_search
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- beam_search
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- modified_beam_search
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- fast_beam_search
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=4,
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help="""An interger indicating how many candidates we will keep for each
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frame. Used only when --decoding-method is beam_search or
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modified_beam_search.""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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default=4,
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help="""A floating point value to calculate the cutoff score during beam
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search (i.e., `cutoff = max-score - beam`), which is the same as the
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`beam` in Kaldi.
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Used only when --decoding-method is fast_beam_search""",
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)
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parser.add_argument(
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"--max-contexts",
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type=int,
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default=4,
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help="""Used only when --decoding-method is
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fast_beam_search""",
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)
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parser.add_argument(
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"--max-states",
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type=int,
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default=8,
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help="""Used only when --decoding-method is
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fast_beam_search""",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; "
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"2 means tri-gram",
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)
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parser.add_argument(
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"--max-sym-per-frame",
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type=int,
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default=1,
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help="""Maximum number of symbols per frame.
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Used only when --decoding_method is greedy_search""",
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)
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parser.add_argument(
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"--simulate-streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Whether to simulate streaming in decoding, this is a good way to
|
||||
test a streaming model.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decode-chunk-size",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The chunk size for decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--left-context",
|
||||
type=int,
|
||||
default=64,
|
||||
help="left context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
batch: dict,
|
||||
decoding_graph: 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 greedy_search is used, it would be "greedy_search"
|
||||
If beam search with a beam size of 7 is used, it would be
|
||||
"beam_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`.
|
||||
model:
|
||||
The neural model.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict.
|
||||
"""
|
||||
device = next(model.parameters()).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)
|
||||
|
||||
feature_lens += params.left_context
|
||||
feature = torch.nn.functional.pad(
|
||||
feature,
|
||||
pad=(0, 0, 0, params.left_context),
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
if params.simulate_streaming:
|
||||
encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
|
||||
x=feature,
|
||||
x_lens=feature_lens,
|
||||
chunk_size=params.decode_chunk_size,
|
||||
left_context=params.left_context,
|
||||
simulate_streaming=True,
|
||||
)
|
||||
else:
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
x=feature, x_lens=feature_lens
|
||||
)
|
||||
|
||||
hyps = []
|
||||
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
hyp_tokens = fast_beam_search_one_best(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
elif (
|
||||
params.decoding_method == "greedy_search"
|
||||
and params.max_sym_per_frame == 1
|
||||
):
|
||||
hyp_tokens = greedy_search_batch(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
hyp_tokens = modified_beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
beam=params.beam_size,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
else:
|
||||
batch_size = encoder_out.size(0)
|
||||
|
||||
for i in range(batch_size):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
elif params.decoding_method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp])
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
return {"greedy_search": hyps}
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
return {
|
||||
(
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
): hyps
|
||||
}
|
||||
else:
|
||||
return {f"beam_size_{params.beam_size}": hyps}
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[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.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 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 = "?"
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
log_interval = 100
|
||||
else:
|
||||
log_interval = 2
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
texts = [list(str(text)) for text in texts]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
lexicon=lexicon,
|
||||
decoding_graph=decoding_graph,
|
||||
batch=batch,
|
||||
)
|
||||
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for hyp_words, ref_text in zip(hyps, texts):
|
||||
this_batch.append((ref_text, hyp_words))
|
||||
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
if batch_idx % log_interval == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(
|
||||
f"batch {batch_str}, cuts processed until now is {num_cuts}"
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
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}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
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}-{key}-{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()
|
||||
WenetSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
assert params.decoding_method in (
|
||||
"greedy_search",
|
||||
"beam_search",
|
||||
"fast_beam_search",
|
||||
"modified_beam_search",
|
||||
)
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
if "fast_beam_search" in params.decoding_method:
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
elif "beam_search" in params.decoding_method:
|
||||
params.suffix += f"-beam-{params.beam_size}"
|
||||
else:
|
||||
params.suffix += f"-context-{params.context_size}"
|
||||
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
|
||||
|
||||
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}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
|
||||
if params.simulate_streaming:
|
||||
assert (
|
||||
params.causal_convolution
|
||||
), "Decoding in streaming requires causal convolution"
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_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()
|
||||
model.device = device
|
||||
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
else:
|
||||
decoding_graph = None
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
# Note: Please use "pip install webdataset==0.1.103"
|
||||
# for installing the webdataset.
|
||||
import glob
|
||||
import os
|
||||
|
||||
from lhotse import CutSet
|
||||
from lhotse.dataset.webdataset import export_to_webdataset
|
||||
|
||||
wenetspeech = WenetSpeechAsrDataModule(args)
|
||||
|
||||
dev = "dev"
|
||||
test_net = "test_net"
|
||||
test_meeting = "test_meeting"
|
||||
|
||||
if not os.path.exists(f"{dev}/shared-0.tar"):
|
||||
os.makedirs(dev)
|
||||
dev_cuts = wenetspeech.valid_cuts()
|
||||
export_to_webdataset(
|
||||
dev_cuts,
|
||||
output_path=f"{dev}/shared-%d.tar",
|
||||
shard_size=300,
|
||||
)
|
||||
|
||||
if not os.path.exists(f"{test_net}/shared-0.tar"):
|
||||
os.makedirs(test_net)
|
||||
test_net_cuts = wenetspeech.test_net_cuts()
|
||||
export_to_webdataset(
|
||||
test_net_cuts,
|
||||
output_path=f"{test_net}/shared-%d.tar",
|
||||
shard_size=300,
|
||||
)
|
||||
|
||||
if not os.path.exists(f"{test_meeting}/shared-0.tar"):
|
||||
os.makedirs(test_meeting)
|
||||
test_meeting_cuts = wenetspeech.test_meeting_cuts()
|
||||
export_to_webdataset(
|
||||
test_meeting_cuts,
|
||||
output_path=f"{test_meeting}/shared-%d.tar",
|
||||
shard_size=300,
|
||||
)
|
||||
|
||||
dev_shards = [
|
||||
str(path)
|
||||
for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
|
||||
]
|
||||
cuts_dev_webdataset = CutSet.from_webdataset(
|
||||
dev_shards,
|
||||
split_by_worker=True,
|
||||
split_by_node=True,
|
||||
shuffle_shards=True,
|
||||
)
|
||||
|
||||
test_net_shards = [
|
||||
str(path)
|
||||
for path in sorted(glob.glob(os.path.join(test_net, "shared-*.tar")))
|
||||
]
|
||||
cuts_test_net_webdataset = CutSet.from_webdataset(
|
||||
test_net_shards,
|
||||
split_by_worker=True,
|
||||
split_by_node=True,
|
||||
shuffle_shards=True,
|
||||
)
|
||||
|
||||
test_meeting_shards = [
|
||||
str(path)
|
||||
for path in sorted(
|
||||
glob.glob(os.path.join(test_meeting, "shared-*.tar"))
|
||||
)
|
||||
]
|
||||
cuts_test_meeting_webdataset = CutSet.from_webdataset(
|
||||
test_meeting_shards,
|
||||
split_by_worker=True,
|
||||
split_by_node=True,
|
||||
shuffle_shards=True,
|
||||
)
|
||||
|
||||
dev_dl = wenetspeech.valid_dataloaders(cuts_dev_webdataset)
|
||||
test_net_dl = wenetspeech.test_dataloaders(cuts_test_net_webdataset)
|
||||
test_meeting_dl = wenetspeech.test_dataloaders(cuts_test_meeting_webdataset)
|
||||
|
||||
test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
|
||||
test_dl = [dev_dl, test_net_dl, test_meeting_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dl):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
lexicon=lexicon,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,147 @@
|
||||
# Copyright 2022 Xiaomi Corp. (authors: Wei Kang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import math
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from beam_search import Hypothesis, HypothesisList
|
||||
|
||||
from icefall.utils import AttributeDict
|
||||
|
||||
|
||||
class DecodeStream(object):
|
||||
def __init__(
|
||||
self,
|
||||
params: AttributeDict,
|
||||
initial_states: List[torch.Tensor],
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
device: torch.device = torch.device("cpu"),
|
||||
) -> None:
|
||||
"""
|
||||
Args:
|
||||
initial_states:
|
||||
Initial decode states of the model, e.g. the return value of
|
||||
`get_init_state` in conformer.py
|
||||
decoding_graph:
|
||||
Decoding graph used for decoding, may be a TrivialGraph or a HLG.
|
||||
Used only when decoding_method is fast_beam_search.
|
||||
device:
|
||||
The device to run this stream.
|
||||
"""
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
assert decoding_graph is not None
|
||||
assert device == decoding_graph.device
|
||||
|
||||
self.params = params
|
||||
self.LOG_EPS = math.log(1e-10)
|
||||
|
||||
self.states = initial_states
|
||||
|
||||
# It contains a 2-D tensors representing the feature frames.
|
||||
self.features: torch.Tensor = None
|
||||
|
||||
self.num_frames: int = 0
|
||||
# how many frames have been processed. (before subsampling).
|
||||
# we only modify this value in `func:get_feature_frames`.
|
||||
self.num_processed_frames: int = 0
|
||||
|
||||
self._done: bool = False
|
||||
|
||||
# The transcript of current utterance.
|
||||
self.ground_truth: str = ""
|
||||
|
||||
# The decoding result (partial or final) of current utterance.
|
||||
self.hyp: List = []
|
||||
|
||||
# how many frames have been processed, after subsampling (i.e. a
|
||||
# cumulative sum of the second return value of
|
||||
# encoder.streaming_forward
|
||||
self.done_frames: int = 0
|
||||
|
||||
self.pad_length = (
|
||||
params.right_context + 2
|
||||
) * params.subsampling_factor + 3
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
self.hyp = [params.blank_id] * params.context_size
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
self.hyps = HypothesisList()
|
||||
self.hyps.add(
|
||||
Hypothesis(
|
||||
ys=[params.blank_id] * params.context_size,
|
||||
log_prob=torch.zeros(1, dtype=torch.float32, device=device),
|
||||
)
|
||||
)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
# The rnnt_decoding_stream for fast_beam_search.
|
||||
self.rnnt_decoding_stream: k2.RnntDecodingStream = (
|
||||
k2.RnntDecodingStream(decoding_graph)
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
|
||||
@property
|
||||
def done(self) -> bool:
|
||||
"""Return True if all the features are processed."""
|
||||
return self._done
|
||||
|
||||
def set_features(
|
||||
self,
|
||||
features: torch.Tensor,
|
||||
) -> None:
|
||||
"""Set features tensor of current utterance."""
|
||||
assert features.dim() == 2, features.dim()
|
||||
self.features = torch.nn.functional.pad(
|
||||
features,
|
||||
(0, 0, 0, self.pad_length),
|
||||
mode="constant",
|
||||
value=self.LOG_EPS,
|
||||
)
|
||||
self.num_frames = self.features.size(0)
|
||||
|
||||
def get_feature_frames(self, chunk_size: int) -> Tuple[torch.Tensor, int]:
|
||||
"""Consume chunk_size frames of features"""
|
||||
chunk_length = chunk_size + self.pad_length
|
||||
|
||||
ret_length = min(
|
||||
self.num_frames - self.num_processed_frames, chunk_length
|
||||
)
|
||||
|
||||
ret_features = self.features[
|
||||
self.num_processed_frames : self.num_processed_frames # noqa
|
||||
+ ret_length
|
||||
]
|
||||
|
||||
self.num_processed_frames += chunk_size
|
||||
if self.num_processed_frames >= self.num_frames:
|
||||
self._done = True
|
||||
|
||||
return ret_features, ret_length
|
||||
|
||||
def decoding_result(self) -> List[int]:
|
||||
"""Obtain current decoding result."""
|
||||
if self.params.decoding_method == "greedy_search":
|
||||
return self.hyp[self.params.context_size :] # noqa
|
||||
elif self.params.decoding_method == "modified_beam_search":
|
||||
best_hyp = self.hyps.get_most_probable(length_norm=True)
|
||||
return best_hyp.ys[self.params.context_size :] # noqa
|
||||
else:
|
||||
assert self.params.decoding_method == "fast_beam_search"
|
||||
return self.hyp
|
||||
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/decoder.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/decoder.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/decoder.py
|
||||
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/encoder_interface.py
|
||||
209
egs/wenetspeech/ASR/pruned_transducer_stateless5/export.py
Normal file
209
egs/wenetspeech/ASR/pruned_transducer_stateless5/export.py
Normal file
@ -0,0 +1,209 @@
|
||||
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
|
||||
#
|
||||
# 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 converts several saved checkpoints
|
||||
# to a single one using model averaging.
|
||||
"""
|
||||
Usage for offline:
|
||||
./pruned_transducer_stateless5/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_offline \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 4 \
|
||||
--avg 1
|
||||
|
||||
It will generate a file exp_dir/pretrained.pt for offline ASR.
|
||||
|
||||
./pruned_transducer_stateless5/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_offline \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 4 \
|
||||
--avg 1 \
|
||||
--jit True
|
||||
|
||||
It will generate a file exp_dir/cpu_jit.pt for offline ASR.
|
||||
|
||||
Usage for streaming:
|
||||
./pruned_transducer_stateless5/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_streaming \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 7 \
|
||||
--avg 1
|
||||
|
||||
It will generate a file exp_dir/pretrained.pt for streaming ASR.
|
||||
|
||||
./pruned_transducer_stateless5/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_streaming \
|
||||
--lang-dir data/lang_char \
|
||||
--epoch 7 \
|
||||
--avg 1 \
|
||||
--jit True
|
||||
|
||||
It will generate a file exp_dir/cpu_jit.pt for streaming ASR.
|
||||
|
||||
To use the generated file with `pruned_transducer_stateless5/decode.py`,
|
||||
you can do:
|
||||
|
||||
cd /path/to/exp_dir
|
||||
ln -s pretrained.pt epoch-9999.pt
|
||||
|
||||
cd /path/to/egs/wenetspeech/ASR
|
||||
./pruned_transducer_stateless5/decode.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--epoch 4 \
|
||||
--avg 1 \
|
||||
--decoding-method greedy_search \
|
||||
--max-duration 100 \
|
||||
--lang-dir data/lang_char
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=28,
|
||||
help="It specifies the checkpoint to use for decoding."
|
||||
"Note: Epoch counts from 0.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch'. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="pruned_transducer_stateless5/exp",
|
||||
help="""It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="The lang dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--jit",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""True to save a model after applying torch.jit.script.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--context-size",
|
||||
type=int,
|
||||
default=2,
|
||||
help="The context size in the decoder. 1 means bigram; "
|
||||
"2 means tri-gram",
|
||||
)
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def main():
|
||||
args = get_parser().parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
model.to(device)
|
||||
if 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 start >= 0:
|
||||
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))
|
||||
|
||||
model.eval()
|
||||
|
||||
model.to("cpu")
|
||||
model.eval()
|
||||
|
||||
if params.jit:
|
||||
# We won't use the forward() method of the model in C++, so just ignore
|
||||
# it here.
|
||||
# Otherwise, one of its arguments is a ragged tensor and is not
|
||||
# torch scriptabe.
|
||||
model.__class__.forward = torch.jit.ignore(model.__class__.forward)
|
||||
logging.info("Using torch.jit.script")
|
||||
model = torch.jit.script(model)
|
||||
filename = params.exp_dir / "cpu_jit.pt"
|
||||
model.save(str(filename))
|
||||
logging.info(f"Saved to {filename}")
|
||||
else:
|
||||
logging.info("Not using torch.jit.script")
|
||||
# Save it using a format so that it can be loaded
|
||||
# by :func:`load_checkpoint`
|
||||
filename = params.exp_dir / "pretrained.pt"
|
||||
torch.save({"model": model.state_dict()}, str(filename))
|
||||
logging.info(f"Saved to {filename}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = (
|
||||
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
)
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/joiner.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/joiner.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/joiner.py
|
||||
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/model.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/model.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/model.py
|
||||
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/optim.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/optim.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/optim.py
|
||||
343
egs/wenetspeech/ASR/pruned_transducer_stateless5/pretrained.py
Normal file
343
egs/wenetspeech/ASR/pruned_transducer_stateless5/pretrained.py
Normal file
@ -0,0 +1,343 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
|
||||
# 2022 Xiaomi Crop. (authors: Mingshuang Luo)
|
||||
#
|
||||
# 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.
|
||||
"""
|
||||
Offline Usage:
|
||||
(1) greedy search
|
||||
./pruned_transducer_stateless5/pretrained.py \
|
||||
--checkpoint ./pruned_transducer_stateless5/exp_L_offline/pretrained.pt \
|
||||
--lang-dir ./data/lang_char \
|
||||
--method greedy_search \
|
||||
--max-sym-per-frame 1 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(2) modified beam search
|
||||
./pruned_transducer_stateless5/pretrained.py \
|
||||
--checkpoint ./pruned_transducer_stateless5/exp_L_offline/pretrained.pt \
|
||||
--lang-dir ./data/lang_char \
|
||||
--method modified_beam_search \
|
||||
--beam-size 4 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
(3) fast beam search
|
||||
./pruned_transducer_stateless5/pretrained.py \
|
||||
--checkpoint ./pruned_transducer_stateless/exp_L_offline/pretrained.pt \
|
||||
--lang-dir ./data/lang_char \
|
||||
--method fast_beam_search \
|
||||
--beam 4 \
|
||||
--max-contexts 4 \
|
||||
--max-states 8 \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
You can also use `./pruned_transducer_stateless5/exp_L_offline/epoch-xx.pt`.
|
||||
Note: ./pruned_transducer_stateless5/exp_L_offline/pretrained.pt is generated by
|
||||
./pruned_transducer_stateless5/export.py
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import torch
|
||||
import torchaudio
|
||||
from beam_search import (
|
||||
beam_search,
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
greedy_search_batch,
|
||||
modified_beam_search,
|
||||
)
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.lexicon import Lexicon
|
||||
|
||||
|
||||
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(
|
||||
"--lang-dir",
|
||||
type=str,
|
||||
help="""Path to lang.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Possible values are:
|
||||
- greedy_search
|
||||
- modified_beam_search
|
||||
- fast_beam_search
|
||||
""",
|
||||
)
|
||||
|
||||
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.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=48000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam-size",
|
||||
type=int,
|
||||
default=4,
|
||||
help="Used only when --method is beam_search and modified_beam_search ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=4,
|
||||
help="""A floating point value to calculate the cutoff score during beam
|
||||
search (i.e., `cutoff = max-score - beam`), which is the same as the
|
||||
`beam` in Kaldi.
|
||||
Used only when --decoding-method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--max-sym-per-frame",
|
||||
type=int,
|
||||
default=1,
|
||||
help="""Maximum number of symbols per frame. Used only when
|
||||
--method is greedy_search.
|
||||
""",
|
||||
)
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float
|
||||
) -> 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])
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
|
||||
params.update(vars(args))
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
|
||||
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_transducer_model(params)
|
||||
|
||||
checkpoint = torch.load(args.checkpoint, map_location="cpu")
|
||||
model.load_state_dict(checkpoint["model"], strict=False)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
else:
|
||||
decoding_graph = None
|
||||
|
||||
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)
|
||||
|
||||
with torch.no_grad():
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
x=features, x_lens=feature_lengths
|
||||
)
|
||||
|
||||
hyps = []
|
||||
msg = f"Using {params.decoding_method}"
|
||||
logging.info(msg)
|
||||
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
hyp_tokens = fast_beam_search_one_best(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
elif (
|
||||
params.decoding_method == "greedy_search"
|
||||
and params.max_sym_per_frame == 1
|
||||
):
|
||||
hyp_tokens = greedy_search_batch(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
hyp_tokens = modified_beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
for i in range(encoder_out.size(0)):
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
|
||||
else:
|
||||
batch_size = encoder_out.size(0)
|
||||
|
||||
for i in range(batch_size):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
elif params.decoding_method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
hyps.append([lexicon.token_table[idx] for idx in hyp])
|
||||
|
||||
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
egs/wenetspeech/ASR/pruned_transducer_stateless5/scaling.py
Symbolic link
1
egs/wenetspeech/ASR/pruned_transducer_stateless5/scaling.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless5/scaling.py
|
||||
@ -0,0 +1,287 @@
|
||||
# Copyright 2022 Xiaomi Corp. (authors: Wei Kang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import warnings
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from beam_search import Hypothesis, HypothesisList, get_hyps_shape
|
||||
from decode_stream import DecodeStream
|
||||
|
||||
from icefall.decode import one_best_decoding
|
||||
from icefall.utils import get_texts
|
||||
|
||||
|
||||
def greedy_search(
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
streams: List[DecodeStream],
|
||||
) -> None:
|
||||
"""Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
|
||||
Args:
|
||||
model:
|
||||
The transducer model.
|
||||
encoder_out:
|
||||
Output from the encoder. Its shape is (N, T, C), where N >= 1.
|
||||
streams:
|
||||
A list of Stream objects.
|
||||
"""
|
||||
assert len(streams) == encoder_out.size(0)
|
||||
assert encoder_out.ndim == 3
|
||||
|
||||
blank_id = model.decoder.blank_id
|
||||
context_size = model.decoder.context_size
|
||||
device = model.device
|
||||
T = encoder_out.size(1)
|
||||
|
||||
decoder_input = torch.tensor(
|
||||
[stream.hyp[-context_size:] for stream in streams],
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
# decoder_out is of shape (N, 1, decoder_out_dim)
|
||||
decoder_out = model.decoder(decoder_input, need_pad=False)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
|
||||
for t in range(T):
|
||||
# current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
|
||||
current_encoder_out = encoder_out[:, t : t + 1, :] # noqa
|
||||
# print(current_encoder_out.shape)
|
||||
|
||||
logits = model.joiner(
|
||||
current_encoder_out.unsqueeze(2),
|
||||
decoder_out.unsqueeze(1),
|
||||
project_input=False,
|
||||
)
|
||||
# logits'shape (batch_size, vocab_size)
|
||||
logits = logits.squeeze(1).squeeze(1)
|
||||
|
||||
assert logits.ndim == 2, logits.shape
|
||||
y = logits.argmax(dim=1).tolist()
|
||||
emitted = False
|
||||
for i, v in enumerate(y):
|
||||
if v != blank_id:
|
||||
streams[i].hyp.append(v)
|
||||
emitted = True
|
||||
if emitted:
|
||||
# update decoder output
|
||||
decoder_input = torch.tensor(
|
||||
[stream.hyp[-context_size:] for stream in streams],
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
decoder_out = model.decoder(
|
||||
decoder_input,
|
||||
need_pad=False,
|
||||
)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
|
||||
|
||||
def modified_beam_search(
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
streams: List[DecodeStream],
|
||||
num_active_paths: int = 4,
|
||||
) -> None:
|
||||
"""Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
|
||||
Args:
|
||||
model:
|
||||
The RNN-T model.
|
||||
encoder_out:
|
||||
A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of
|
||||
the encoder model.
|
||||
streams:
|
||||
A list of stream objects.
|
||||
num_active_paths:
|
||||
Number of active paths during the beam search.
|
||||
"""
|
||||
assert encoder_out.ndim == 3, encoder_out.shape
|
||||
assert len(streams) == encoder_out.size(0)
|
||||
|
||||
blank_id = model.decoder.blank_id
|
||||
context_size = model.decoder.context_size
|
||||
device = next(model.parameters()).device
|
||||
batch_size = len(streams)
|
||||
T = encoder_out.size(1)
|
||||
|
||||
B = [stream.hyps for stream in streams]
|
||||
|
||||
for t in range(T):
|
||||
current_encoder_out = encoder_out[:, t].unsqueeze(1).unsqueeze(1)
|
||||
# current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
|
||||
|
||||
hyps_shape = get_hyps_shape(B).to(device)
|
||||
|
||||
A = [list(b) for b in B]
|
||||
B = [HypothesisList() for _ in range(batch_size)]
|
||||
|
||||
ys_log_probs = torch.stack(
|
||||
[hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0
|
||||
) # (num_hyps, 1)
|
||||
|
||||
decoder_input = torch.tensor(
|
||||
[hyp.ys[-context_size:] for hyps in A for hyp in hyps],
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
) # (num_hyps, context_size)
|
||||
|
||||
decoder_out = model.decoder(decoder_input, need_pad=False)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
# decoder_out is of shape (num_hyps, 1, 1, decoder_output_dim)
|
||||
|
||||
# Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
|
||||
# as index, so we use `to(torch.int64)` below.
|
||||
current_encoder_out = torch.index_select(
|
||||
current_encoder_out,
|
||||
dim=0,
|
||||
index=hyps_shape.row_ids(1).to(torch.int64),
|
||||
) # (num_hyps, encoder_out_dim)
|
||||
|
||||
logits = model.joiner(
|
||||
current_encoder_out,
|
||||
decoder_out.unsqueeze(1),
|
||||
project_input=False,
|
||||
)
|
||||
# logits is of shape (num_hyps, 1, 1, vocab_size)
|
||||
|
||||
logits = logits.squeeze(1).squeeze(1)
|
||||
|
||||
log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size)
|
||||
|
||||
log_probs.add_(ys_log_probs)
|
||||
|
||||
vocab_size = log_probs.size(-1)
|
||||
|
||||
log_probs = log_probs.reshape(-1)
|
||||
|
||||
row_splits = hyps_shape.row_splits(1) * vocab_size
|
||||
log_probs_shape = k2.ragged.create_ragged_shape2(
|
||||
row_splits=row_splits, cached_tot_size=log_probs.numel()
|
||||
)
|
||||
ragged_log_probs = k2.RaggedTensor(
|
||||
shape=log_probs_shape, value=log_probs
|
||||
)
|
||||
|
||||
for i in range(batch_size):
|
||||
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(
|
||||
num_active_paths
|
||||
)
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
|
||||
topk_token_indexes = (topk_indexes % vocab_size).tolist()
|
||||
|
||||
for k in range(len(topk_hyp_indexes)):
|
||||
hyp_idx = topk_hyp_indexes[k]
|
||||
hyp = A[i][hyp_idx]
|
||||
|
||||
new_ys = hyp.ys[:]
|
||||
new_token = topk_token_indexes[k]
|
||||
if new_token != blank_id:
|
||||
new_ys.append(new_token)
|
||||
|
||||
new_log_prob = topk_log_probs[k]
|
||||
new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
|
||||
B[i].add(new_hyp)
|
||||
|
||||
for i in range(batch_size):
|
||||
streams[i].hyps = B[i]
|
||||
|
||||
|
||||
def fast_beam_search_one_best(
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
processed_lens: torch.Tensor,
|
||||
streams: List[DecodeStream],
|
||||
beam: float,
|
||||
max_states: int,
|
||||
max_contexts: int,
|
||||
) -> None:
|
||||
"""It limits the maximum number of symbols per frame to 1.
|
||||
A lattice is first generated by Fsa-based beam search, then we get the
|
||||
recognition by applying shortest path on the lattice.
|
||||
Args:
|
||||
model:
|
||||
An instance of `Transducer`.
|
||||
encoder_out:
|
||||
A tensor of shape (N, T, C) from the encoder.
|
||||
processed_lens:
|
||||
A tensor of shape (N,) containing the number of processed frames
|
||||
in `encoder_out` before padding.
|
||||
streams:
|
||||
A list of stream objects.
|
||||
beam:
|
||||
Beam value, similar to the beam used in Kaldi..
|
||||
max_states:
|
||||
Max states per stream per frame.
|
||||
max_contexts:
|
||||
Max contexts pre stream per frame.
|
||||
"""
|
||||
assert encoder_out.ndim == 3
|
||||
B, T, C = encoder_out.shape
|
||||
assert B == len(streams)
|
||||
|
||||
context_size = model.decoder.context_size
|
||||
vocab_size = model.decoder.vocab_size
|
||||
|
||||
config = k2.RnntDecodingConfig(
|
||||
vocab_size=vocab_size,
|
||||
decoder_history_len=context_size,
|
||||
beam=beam,
|
||||
max_contexts=max_contexts,
|
||||
max_states=max_states,
|
||||
)
|
||||
individual_streams = []
|
||||
for i in range(B):
|
||||
individual_streams.append(streams[i].rnnt_decoding_stream)
|
||||
decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
|
||||
|
||||
for t in range(T):
|
||||
# shape is a RaggedShape of shape (B, context)
|
||||
# contexts is a Tensor of shape (shape.NumElements(), context_size)
|
||||
shape, contexts = decoding_streams.get_contexts()
|
||||
# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
|
||||
contexts = contexts.to(torch.int64)
|
||||
# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
|
||||
decoder_out = model.decoder(contexts, need_pad=False)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
# current_encoder_out is of shape
|
||||
# (shape.NumElements(), 1, joiner_dim)
|
||||
# fmt: off
|
||||
current_encoder_out = torch.index_select(
|
||||
encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
|
||||
)
|
||||
# fmt: on
|
||||
logits = model.joiner(
|
||||
current_encoder_out.unsqueeze(2),
|
||||
decoder_out.unsqueeze(1),
|
||||
project_input=False,
|
||||
)
|
||||
logits = logits.squeeze(1).squeeze(1)
|
||||
log_probs = logits.log_softmax(dim=-1)
|
||||
decoding_streams.advance(log_probs)
|
||||
|
||||
decoding_streams.terminate_and_flush_to_streams()
|
||||
|
||||
lattice = decoding_streams.format_output(processed_lens.tolist())
|
||||
best_path = one_best_decoding(lattice)
|
||||
hyp_tokens = get_texts(best_path)
|
||||
|
||||
for i in range(B):
|
||||
streams[i].hyp = hyp_tokens[i]
|
||||
@ -0,0 +1,678 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang)
|
||||
#
|
||||
# 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) greedy search
|
||||
python pruned_transducer_stateless5/streaming_decode.py \
|
||||
--epoch 7 \
|
||||
--avg 1 \
|
||||
--decode-chunk-size 16 \
|
||||
--left-context 64 \
|
||||
--right-context 0 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_streaming \
|
||||
--decoding-method greedy_search \
|
||||
--num-decode-streams 2000
|
||||
|
||||
(2) modified beam search
|
||||
python pruned_transducer_stateless5/streaming_decode.py \
|
||||
--epoch 7 \
|
||||
--avg 1 \
|
||||
--decode-chunk-size 16 \
|
||||
--left-context 64 \
|
||||
--right-context 0 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_streaming \
|
||||
--decoding-method modified_beam_search \
|
||||
--num-decode-streams 2000
|
||||
|
||||
(3) fast beam search
|
||||
python pruned_transducer_stateless5/streaming_decode.py \
|
||||
--epoch 7 \
|
||||
--avg 1 \
|
||||
--decode-chunk-size 16 \
|
||||
--left-context 64 \
|
||||
--right-context 0 \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp_L_streaming \
|
||||
--decoding-method fast_beam_search \
|
||||
--num-decode-streams 2000
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import WenetSpeechAsrDataModule
|
||||
from decode_stream import DecodeStream
|
||||
from kaldifeat import Fbank, FbankOptions
|
||||
from lhotse import CutSet
|
||||
from streaming_beam_search import (
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
modified_beam_search,
|
||||
)
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
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=28,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 0.
|
||||
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="pruned_transducer_stateless5/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="""The lang dir
|
||||
It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Supported decoding methods are:
|
||||
greedy_search
|
||||
modified_beam_search
|
||||
fast_beam_search
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-active-paths",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""An interger indicating how many candidates we will keep for each
|
||||
frame. Used only when --decoding-method is modified_beam_search.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=4,
|
||||
help="""A floating point value to calculate the cutoff score during beam
|
||||
search (i.e., `cutoff = max-score - beam`), which is the same as the
|
||||
`beam` in Kaldi.
|
||||
Used only when --decoding-method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=32,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--decode-chunk-size",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The chunk size for decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--left-context",
|
||||
type=int,
|
||||
default=64,
|
||||
help="left context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--right-context",
|
||||
type=int,
|
||||
default=0,
|
||||
help="right context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-decode-streams",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The number of streams that can be decoded parallel.",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def decode_one_chunk(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
decode_streams: List[DecodeStream],
|
||||
) -> List[int]:
|
||||
"""Decode one chunk frames of features for each decode_streams and
|
||||
return the indexes of finished streams in a List.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
decode_streams:
|
||||
A List of DecodeStream, each belonging to a utterance.
|
||||
Returns:
|
||||
Return a List containing which DecodeStreams are finished.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
features = []
|
||||
feature_lens = []
|
||||
states = []
|
||||
|
||||
processed_lens = []
|
||||
|
||||
for stream in decode_streams:
|
||||
feat, feat_len = stream.get_feature_frames(
|
||||
params.decode_chunk_size * params.subsampling_factor
|
||||
)
|
||||
features.append(feat)
|
||||
feature_lens.append(feat_len)
|
||||
states.append(stream.states)
|
||||
processed_lens.append(stream.done_frames)
|
||||
|
||||
feature_lens = torch.tensor(feature_lens, device=device)
|
||||
features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
|
||||
|
||||
# if T is less than 7 there will be an error in time reduction layer,
|
||||
# because we subsample features with ((x_len - 1) // 2 - 1) // 2
|
||||
# we plus 2 here because we will cut off one frame on each size of
|
||||
# encoder_embed output as they see invalid paddings. so we need extra 2
|
||||
# frames.
|
||||
tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
|
||||
if features.size(1) < tail_length:
|
||||
pad_length = tail_length - features.size(1)
|
||||
feature_lens += pad_length
|
||||
features = torch.nn.functional.pad(
|
||||
features,
|
||||
(0, 0, 0, pad_length),
|
||||
mode="constant",
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
states = [
|
||||
torch.stack([x[0] for x in states], dim=2),
|
||||
torch.stack([x[1] for x in states], dim=2),
|
||||
]
|
||||
|
||||
processed_lens = torch.tensor(processed_lens, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens, states = model.encoder.streaming_forward(
|
||||
x=features,
|
||||
x_lens=feature_lens,
|
||||
states=states,
|
||||
left_context=params.left_context,
|
||||
right_context=params.right_context,
|
||||
processed_lens=processed_lens,
|
||||
)
|
||||
|
||||
encoder_out = model.joiner.encoder_proj(encoder_out)
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
greedy_search(
|
||||
model=model, encoder_out=encoder_out, streams=decode_streams
|
||||
)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
processed_lens = processed_lens + encoder_out_lens
|
||||
fast_beam_search_one_best(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
processed_lens=processed_lens,
|
||||
streams=decode_streams,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
modified_beam_search(
|
||||
model=model,
|
||||
streams=decode_streams,
|
||||
encoder_out=encoder_out,
|
||||
num_active_paths=params.num_active_paths,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
|
||||
states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
|
||||
|
||||
finished_streams = []
|
||||
for i in range(len(decode_streams)):
|
||||
decode_streams[i].states = [states[0][i], states[1][i]]
|
||||
decode_streams[i].done_frames += encoder_out_lens[i]
|
||||
if decode_streams[i].done:
|
||||
finished_streams.append(i)
|
||||
|
||||
return finished_streams
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
cuts: CutSet,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
cuts:
|
||||
Lhotse Cutset containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 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.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
opts = FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
log_interval = 100
|
||||
|
||||
decode_results = []
|
||||
# Contain decode streams currently running.
|
||||
decode_streams = []
|
||||
initial_states = model.encoder.get_init_state(
|
||||
params.left_context, device=device
|
||||
)
|
||||
for num, cut in enumerate(cuts):
|
||||
# each utterance has a DecodeStream.
|
||||
decode_stream = DecodeStream(
|
||||
params=params,
|
||||
initial_states=initial_states,
|
||||
decoding_graph=decoding_graph,
|
||||
device=device,
|
||||
)
|
||||
|
||||
audio: np.ndarray = cut.load_audio()
|
||||
# audio.shape: (1, num_samples)
|
||||
assert len(audio.shape) == 2
|
||||
assert audio.shape[0] == 1, "Should be single channel"
|
||||
assert audio.dtype == np.float32, audio.dtype
|
||||
|
||||
samples = torch.from_numpy(audio).squeeze(0)
|
||||
|
||||
fbank = Fbank(opts)
|
||||
decode_stream.set_features(fbank(samples.to(device)))
|
||||
decode_stream.ground_truth = cut.supervisions[0].text
|
||||
|
||||
decode_streams.append(decode_stream)
|
||||
|
||||
while len(decode_streams) >= params.num_decode_streams:
|
||||
finished_streams = decode_one_chunk(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].decoding_result()
|
||||
decode_results.append(
|
||||
(
|
||||
list(decode_streams[i].ground_truth),
|
||||
[lexicon.token_table[idx] for idx in hyp],
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if num % log_interval == 0:
|
||||
logging.info(f"Cuts processed until now is {num}.")
|
||||
|
||||
# decode final chunks of last sequences
|
||||
while len(decode_streams):
|
||||
finished_streams = decode_one_chunk(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].decoding_result()
|
||||
decode_results.append(
|
||||
(
|
||||
list(decode_streams[i].ground_truth),
|
||||
[lexicon.token_table[idx] for idx in hyp],
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
key = "greedy_search"
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
key = (
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
key = f"num_active_paths_{params.num_active_paths}"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
|
||||
return {key: decode_results}
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[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}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
# sort results so we can easily compare the difference between two
|
||||
# recognition results
|
||||
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}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
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}-{key}-{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()
|
||||
WenetSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
params.res_dir = params.exp_dir / "streaming" / 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}"
|
||||
|
||||
# for streaming
|
||||
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context}"
|
||||
params.suffix += f"-right-context-{params.right_context}"
|
||||
|
||||
# for fast_beam_search
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
|
||||
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}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
|
||||
params.causal_convolution = True
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_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()
|
||||
model.device = device
|
||||
|
||||
decoding_graph = None
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
wenetspeech = WenetSpeechAsrDataModule(args)
|
||||
|
||||
dev_cuts = wenetspeech.valid_cuts()
|
||||
test_net_cuts = wenetspeech.test_net_cuts()
|
||||
test_meeting_cuts = wenetspeech.test_meeting_cuts()
|
||||
|
||||
test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
|
||||
test_cuts = [dev_cuts, test_net_cuts, test_meeting_cuts]
|
||||
|
||||
for test_set, test_cut in zip(test_sets, test_cuts):
|
||||
results_dict = decode_dataset(
|
||||
cuts=test_cut,
|
||||
params=params,
|
||||
model=model,
|
||||
lexicon=lexicon,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1217
egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py
Executable file
1217
egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py
Executable file
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user