ESPnet: end-to-end speech processing toolkit
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Docs | Example | Example (ESPnet2) | Docker | Notebook | Tutorial (2019)
ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition and end-to-end text-to-speech. ESPnet uses chainer and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.
Key Features
Kaldi style complete recipe
- Support numbers of
ASRrecipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, etc.) - Support numbers of
TTSrecipes with a similar manner to the ASR recipe (LJSpeech, LibriTTS, M-AILABS, etc.) - Support numbers of
STrecipes (Fisher-CallHome Spanish, Libri-trans, IWSLT'18, How2, Must-C, Mboshi-French, etc.) - Support numbers of
MTrecipes (IWSLT'16, the above ST recipes etc.) - Support speech separation and recognition recipe (WSJ-2mix)
- Support voice conversion recipe (VCC2020 baseline) (new!)
- Support speech language understanding recipe (FSC baseline) (new!)
ASR: Automatic Speech Recognition
- State-of-the-art performance in several ASR benchmarks (comparable/superior to hybrid DNN/HMM and CTC)
- Hybrid CTC/attention based end-to-end ASR
- Fast/accurate training with CTC/attention multitask training
- CTC/attention joint decoding to boost monotonic alignment decoding
- Encoder: VGG-like CNN + BiRNN (LSTM/GRU), sub-sampling BiRNN (LSTM/GRU) or Transformer
- Attention: Dot product, location-aware attention, variants of multihead
- Incorporate RNNLM/LSTMLM/TransformerLM/N-gram trained only with text data
- Batch GPU decoding
- Transducer based end-to-end ASR
- Architecture:
- RNN-based encoder and decoder.
- Custom encoder and decoder supporting Transformer, Conformer (encoder), TDNN (encoder) and causal Conv1D (decoder) blocks.
- VGG2L (RNN/custom encoder) and Conv2D (custom encoder) bottlenecks.
- Search algorithms:
- Greedy search constrained to one emission by timestep.
- Default beam search algorithm without prefix search.
- Alignment-Length Synchronous decoding (Saon et al., 2020).
- Time Synchronous Decoding (Saon et al., 2020).
- N-step Constrained beam search modified from Kim et al., 2020.
- modified Adaptive Expansion Search based on Kim et al. (2021) and NSC.
- Features:
- Multi-task learning with various auxiliary tasks: CTC, Label smoothing, auxiliary RNN-T and symmetric KL divergence.
- Transfer learning with acoustic model and/or language model.
- Training with FastEmit regularization method.
Please refer to the tutorial page for complete documentation.
- Architecture:
- CTC segmentation
- Non-autoregressive model based on Mask-CTC
- ASR examples for supporting endangered language documentation (Please refer to egs/puebla_nahuatl and egs/yoloxochitl_mixtec for details)
- Wav2Vec2.0 pretrained model as Encoder, imported from FairSeq.
- Self-supervised learning representations as features, using upstream models in S3PRL in frontend.
Demonstration
- Real-time ASR demo with ESPnet2
- Gradio Web Demo on Huggingface Spaces. Check out the Web Demo
TTS: Text-to-speech
- Architecture
- Tacotron2
- Transformer-TTS
- FastSpeech
- FastSpeech2
- Conformer FastSpeech & FastSpeech2
- VITS
- Multi-speaker & multi-language extention
- Pretrined speaker embedding (e.g., X-vector)
- Speaker ID embedding
- Language ID embedding
- Global style token (GST) embedding
- Mix of the above embeddings
- End-to-end training
- End-to-end text-to-wav model (e.g., VITS)
- Joint training of text2mel and vocoder
- Various language support
- En / Jp / Zn / De / Ru / And more...
- Integration with neural vocoders
- Parallel WaveGAN
- MelGAN
- Multi-band MelGAN
- HiFiGAN
- StyleMelGAN
- Mix of the above models
Demonstration
- Real-time TTS demo with ESPnet2
To train the neural vocoder, please check the following repositories:
NOTE:
- We are moving on ESPnet2-based development for TTS.
- If you are beginner, we recommend using ESPnet2-TTS.
SE: Speech enhancement (and separation)
- Single-speaker speech enhancement
- Multi-speaker speech separation
- Unified encoder-separator-decoder structure for time-domain and frequency-domain models
- Encoder/Decoder: STFT/iSTFT, Convolution/Transposed-Convolution
- Separators: BLSTM, Transformer, Conformer, DPRNN, Neural Beamformers, etc.
- Flexible ASR integration: working as an individual task or as the ASR frontend
- Easy to import pretrained models from Asteroid
- Both the pre-trained models from Asteroid and the specific configuration are supported.
Demonstration
- Interactive SE demo with ESPnet2
ST: Speech Translation & MT: Machine Translation
- State-of-the-art performance in several ST benchmarks (comparable/superior to cascaded ASR and MT)
- Transformer based end-to-end ST (new!)
- Transformer based end-to-end MT (new!)
VC: Voice conversion
- Transformer and Tacotron2 based parallel VC using melspectrogram (new!)
- End-to-end VC based on cascaded ASR+TTS (Baseline system for Voice Conversion Challenge 2020!)
SLU: Speech Language Understanding
- Predicting intent by directly classifying it as one of intent or decoding by character
- Transformer & RNN based encoder-decoder model
- Establish SOTA results with spectral augmentation (Performs better than reported results of pretrained model on Fluent Speech Command Dataset)
DNN Framework
- Flexible network architecture thanks to chainer and pytorch
- Flexible front-end processing thanks to kaldiio and HDF5 support
- Tensorboard based monitoring
ESPnet2
See ESPnet2.
- Independent from Kaldi/Chainer, unlike ESPnet1
- On the fly feature extraction and text processing when training
- Supporting DistributedDataParallel and DaraParallel both
- Supporting multiple nodes training and integrated with Slurm or MPI
- Supporting Sharded Training provided by fairscale
- A template recipe which can be applied for all corpora
- Possible to train any size of corpus without CPU memory error
- ESPnet Model Zoo
- Integrated with wandb
Installation
-
If you intend to do full experiments including DNN training, then see Installation.
-
If you just need the Python module only:
pip install espnet # To install latest # pip install git+https://github.com/espnet/espnet
You need to install some packages.
pip install torch pip install chainer==6.0.0 cupy==6.0.0 # [Option] If you'll use ESPnet1 pip install torchaudio # [Option] If you'll use enhancement task pip install torch_optimizer # [Option] If you'll use additional optimizers in ESPnet2
There are some required packages depending on each task other than above. If you meet ImportError, please install them at that time.
-
(ESPnet2) Once installed, run
wandb loginand set--use_wandb trueto enable tracking runs using W&B.
Usage
See Usage.
Docker Container
go to docker/ and follow instructions.
Contribution
Thank you for taking times for ESPnet! Any contributions to ESPnet are welcome and feel free to ask any questions or requests to issues. If it's the first contribution to ESPnet for you, please follow the contribution guide.
Results and demo
You can find useful tutorials and demos in Interspeech 2019 Tutorial
ASR results
expand
We list the character error rate (CER) and word error rate (WER) of major ASR tasks.
| Task | CER (%) | WER (%) | Pretrained model |
|---|---|---|---|
| Aishell dev/test | 4.6/5.1 | N/A | link |
| ESPnet2 Aishell dev/test | 4.4/4.7 | N/A | link |
| Common Voice dev/test | 1.7/1.8 | 2.2/2.3 | link |
| CSJ eval1/eval2/eval3 | 5.7/3.8/4.2 | N/A | link |
| ESPnet2 CSJ eval1/eval2/eval3 | 4.5/3.3/3.6 | N/A | link |
| HKUST dev | 23.5 | N/A | link |
| ESPnet2 HKUST dev | 21.2 | N/A | link |
| Librispeech dev_clean/dev_other/test_clean/test_other | N/A | 1.9/4.9/2.1/4.9 | link |
| ESPnet2 Librispeech dev_clean/dev_other/test_clean/test_other | 0.6/1.5/0.6/1.4 | 1.7/3.4/1.8/3.6 | link |
| Switchboard (eval2000) callhm/swbd | N/A | 14.0/6.8 | link |
| TEDLIUM2 dev/test | N/A | 8.6/7.2 | link |
| TEDLIUM3 dev/test | N/A | 9.6/7.6 | link |
| WSJ dev93/eval92 | 3.2/2.1 | 7.0/4.7 | N/A |
| ESPnet2 WSJ dev93/eval92 | 1.1/0.8 | 2.8/1.8 | link |
Note that the performance of the CSJ, HKUST, and Librispeech tasks was significantly improved by using the wide network (#units = 1024) and large subword units if necessary reported by RWTH.
If you want to check the results of the other recipes, please check egs/<name_of_recipe>/asr1/RESULTS.md.
ASR demo
expand
You can recognize speech in a WAV file using pretrained models. Go to a recipe directory and run utils/recog_wav.sh as follows:
# go to recipe directory and source path of espnet tools
cd egs/tedlium2/asr1 && . ./path.sh
# let's recognize speech!
recog_wav.sh --models tedlium2.transformer.v1 example.wav
where example.wav is a WAV file to be recognized. The sampling rate must be consistent with that of data used in training.
Available pretrained models in the demo script are listed as below.
| Model | Notes |
|---|---|
| tedlium2.rnn.v1 | Streaming decoding based on CTC-based VAD |
| tedlium2.rnn.v2 | Streaming decoding based on CTC-based VAD (batch decoding) |
| tedlium2.transformer.v1 | Joint-CTC attention Transformer trained on Tedlium 2 |
| tedlium3.transformer.v1 | Joint-CTC attention Transformer trained on Tedlium 3 |
| librispeech.transformer.v1 | Joint-CTC attention Transformer trained on Librispeech |
| commonvoice.transformer.v1 | Joint-CTC attention Transformer trained on CommonVoice |
| csj.transformer.v1 | Joint-CTC attention Transformer trained on CSJ |
| csj.rnn.v1 | Joint-CTC attention VGGBLSTM trained on CSJ |
SE results
expand
We list results from three different models on WSJ0-2mix, which is one the most widely used benchmark dataset for speech separation.
| Model | STOI | SAR | SDR | SIR |
|---|---|---|---|---|
| TF Masking | 0.89 | 11.40 | 10.24 | 18.04 |
| Conv-Tasnet | 0.95 | 16.62 | 15.94 | 25.90 |
| DPRNN-Tasnet | 0.96 | 18.82 | 18.29 | 28.92 |
SE demos
expand
It is based on ESPnet2. Pretrained models are available for both speech enhancement and speech separation tasks.
ST results
expand
We list 4-gram BLEU of major ST tasks.
end-to-end system
| Task | BLEU | Pretrained model |
|---|---|---|
| Fisher-CallHome Spanish fisher_test (Es->En) | 51.03 | link |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 20.44 | link |
| Libri-trans test (En->Fr) | 16.70 | link |
| How2 dev5 (En->Pt) | 45.68 | link |
| Must-C tst-COMMON (En->De) | 22.91 | link |
| Mboshi-French dev (Fr->Mboshi) | 6.18 | N/A |
cascaded system
| Task | BLEU | Pretrained model |
|---|---|---|
| Fisher-CallHome Spanish fisher_test (Es->En) | 42.16 | N/A |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 19.82 | N/A |
| Libri-trans test (En->Fr) | 16.96 | N/A |
| How2 dev5 (En->Pt) | 44.90 | N/A |
| Must-C tst-COMMON (En->De) | 23.65 | N/A |
If you want to check the results of the other recipes, please check egs/<name_of_recipe>/st1/RESULTS.md.
ST demo
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(New!) We made a new real-time E2E-ST + TTS demonstration in Google Colab. Please access the notebook from the following button and enjoy the real-time speech-to-speech translation!
You can translate speech in a WAV file using pretrained models. Go to a recipe directory and run utils/translate_wav.sh as follows:
# go to recipe directory and source path of espnet tools
cd egs/fisher_callhome_spanish/st1 && . ./path.sh
# download example wav file
wget -O - https://github.com/espnet/espnet/files/4100928/test.wav.tar.gz | tar zxvf -
# let's translate speech!
translate_wav.sh --models fisher_callhome_spanish.transformer.v1.es-en test.wav
where test.wav is a WAV file to be translated. The sampling rate must be consistent with that of data used in training.
Available pretrained models in the demo script are listed as below.
| Model | Notes |
|---|---|
| fisher_callhome_spanish.transformer.v1 | Transformer-ST trained on Fisher-CallHome Spanish Es->En |
MT results
expand
| Task | BLEU | Pretrained model |
|---|---|---|
| Fisher-CallHome Spanish fisher_test (Es->En) | 61.45 | link |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 29.86 | link |
| Libri-trans test (En->Fr) | 18.09 | link |
| How2 dev5 (En->Pt) | 58.61 | link |
| Must-C tst-COMMON (En->De) | 27.63 | link |
| IWSLT'14 test2014 (En->De) | 24.70 | link |
| IWSLT'14 test2014 (De->En) | 29.22 | link |
| IWSLT'16 test2014 (En->De) | 24.05 | link |
| IWSLT'16 test2014 (De->En) | 29.13 | link |
TTS results
ESPnet2
You can listen to the generated samples in the following URL.
Note that in the generation we use Griffin-Lim (
wav/) and Parallel WaveGAN (wav_pwg/).
You can download pretrained models via espnet_model_zoo.
You can download pretrained vocoders via kan-bayashi/ParallelWaveGAN.
ESPnet1
NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest results in the above ESPnet2 results.
You can listen to our samples in demo HP espnet-tts-sample. Here we list some notable ones:
- Single English speaker Tacotron2
- Single Japanese speaker Tacotron2
- Single other language speaker Tacotron2
- Multi English speaker Tacotron2
- Single English speaker Transformer
- Single English speaker FastSpeech
- Multi English speaker Transformer
- Single Italian speaker FastSpeech
- Single Mandarin speaker Transformer
- Single Mandarin speaker FastSpeech
- Multi Japanese speaker Transformer
- Single English speaker models with Parallel WaveGAN
- Single English speaker knowledge distillation-based FastSpeech
You can download all of the pretrained models and generated samples:
Note that in the generated samples we use the following vocoders: Griffin-Lim (GL), WaveNet vocoder (WaveNet), Parallel WaveGAN (ParallelWaveGAN), and MelGAN (MelGAN). The neural vocoders are based on following repositories.
- kan-bayashi/ParallelWaveGAN: Parallel WaveGAN / MelGAN / Multi-band MelGAN
- r9y9/wavenet_vocoder: 16 bit mixture of Logistics WaveNet vocoder
- kan-bayashi/PytorchWaveNetVocoder: 8 bit Softmax WaveNet Vocoder with the noise shaping
If you want to build your own neural vocoder, please check the above repositories. kan-bayashi/ParallelWaveGAN provides the manual about how to decode ESPnet-TTS model's features with neural vocoders. Please check it.
Here we list all of the pretrained neural vocoders. Please download and enjoy the generation of high quality speech!
| Model link | Lang | Fs [Hz] | Mel range [Hz] | FFT / Shift / Win [pt] | Model type |
|---|---|---|---|---|---|
| ljspeech.wavenet.softmax.ns.v1 | EN | 22.05k | None | 1024 / 256 / None | Softmax WaveNet |
| ljspeech.wavenet.mol.v1 | EN | 22.05k | None | 1024 / 256 / None | MoL WaveNet |
| ljspeech.parallel_wavegan.v1 | EN | 22.05k | None | 1024 / 256 / None | Parallel WaveGAN |
| ljspeech.wavenet.mol.v2 | EN | 22.05k | 80-7600 | 1024 / 256 / None | MoL WaveNet |
| ljspeech.parallel_wavegan.v2 | EN | 22.05k | 80-7600 | 1024 / 256 / None | Parallel WaveGAN |
| ljspeech.melgan.v1 | EN | 22.05k | 80-7600 | 1024 / 256 / None | MelGAN |
| ljspeech.melgan.v3 | EN | 22.05k | 80-7600 | 1024 / 256 / None | MelGAN |
| libritts.wavenet.mol.v1 | EN | 24k | None | 1024 / 256 / None | MoL WaveNet |
| jsut.wavenet.mol.v1 | JP | 24k | 80-7600 | 2048 / 300 / 1200 | MoL WaveNet |
| jsut.parallel_wavegan.v1 | JP | 24k | 80-7600 | 2048 / 300 / 1200 | Parallel WaveGAN |
| csmsc.wavenet.mol.v1 | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | MoL WaveNet |
| csmsc.parallel_wavegan.v1 | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | Parallel WaveGAN |
If you want to use the above pretrained vocoders, please exactly match the feature setting with them.
TTS demo
ESPnet2
You can try the real-time demo in Google Colab. Please access the notebook from the following button and enjoy the real-time synthesis!
- Real-time TTS demo with ESPnet2
English, Japanese, and Mandarin models are available in the demo.
ESPnet1
NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest demo in the above ESPnet2 demo.
You can try the real-time demo in Google Colab. Please access the notebook from the following button and enjoy the real-time synthesis.
- Real-time TTS demo with ESPnet1
We also provide shell script to perform synthesize. Go to a recipe directory and run utils/synth_wav.sh as follows:
# go to recipe directory and source path of espnet tools
cd egs/ljspeech/tts1 && . ./path.sh
# we use upper-case char sequence for the default model.
echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example.txt
# let's synthesize speech!
synth_wav.sh example.txt
# also you can use multiple sentences
echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example_multi.txt
echo "TEXT TO SPEECH IS A TECHNIQUE TO CONVERT TEXT INTO SPEECH." >> example_multi.txt
synth_wav.sh example_multi.txt
You can change the pretrained model as follows:
synth_wav.sh --models ljspeech.fastspeech.v1 example.txt
Waveform synthesis is performed with Griffin-Lim algorithm and neural vocoders (WaveNet and ParallelWaveGAN). You can change the pretrained vocoder model as follows:
synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v1 example.txt
WaveNet vocoder provides very high quality speech but it takes time to generate.
See more details or available models via --help.
synth_wav.sh --help
VC results
expand
- Transformer and Tacotron2 based VC
You can listen to some samples on the demo webpage.
- Cascade ASR+TTS as one of the baseline systems of VCC2020
The Voice Conversion Challenge 2020 (VCC2020) adopts ESPnet to build an end-to-end based baseline system. In VCC2020, the objective is intra/cross lingual nonparallel VC. You can download converted samples of the cascade ASR+TTS baseline system here.
SLU results
ESPnet2
- Transformer based SLU for Fluent Speech Command Dataset
In SLU, The objective is to infer the meaning or intent of spoken utterance. The Fluent Speech Command Dataset describes an intent as combination of 3 slot values: action, object and location. You can see baseline results on this dataset here
CTC Segmentation demo
ESPnet1
CTC segmentation determines utterance segments within audio files. Aligned utterance segments constitute the labels of speech datasets.
As demo, we align start and end of utterances within the audio file ctc_align_test.wav, using the example script utils/asr_align_wav.sh. For preparation, set up a data directory:
cd egs/tedlium2/align1/
# data directory
align_dir=data/demo
mkdir -p ${align_dir}
# wav file
base=ctc_align_test
wav=../../../test_utils/${base}.wav
# recipe files
echo "batchsize: 0" > ${align_dir}/align.yaml
cat << EOF > ${align_dir}/utt_text
${base} THE SALE OF THE HOTELS
${base} IS PART OF HOLIDAY'S STRATEGY
${base} TO SELL OFF ASSETS
${base} AND CONCENTRATE
${base} ON PROPERTY MANAGEMENT
EOF
Here, utt_text is the file containing the list of utterances. Choose a pre-trained ASR model that includes a CTC layer to find utterance segments:
# pre-trained ASR model
model=wsj.transformer_small.v1
mkdir ./conf && cp ../../wsj/asr1/conf/no_preprocess.yaml ./conf
../../../utils/asr_align_wav.sh \
--models ${model} \
--align_dir ${align_dir} \
--align_config ${align_dir}/align.yaml \
${wav} ${align_dir}/utt_text
Segments are written to aligned_segments as a list of file/utterance name, utterance start and end times in seconds and a confidence score. The confidence score is a probability in log space that indicates how good the utterance was aligned. If needed, remove bad utterances:
min_confidence_score=-5
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' ${align_dir}/aligned_segments
The demo script utils/ctc_align_wav.sh uses an already pretrained ASR model (see list above for more models). It is recommended to use models with RNN-based encoders (such as BLSTMP) for aligning large audio files; rather than using Transformer models that have a high memory consumption on longer audio data. The sample rate of the audio must be consistent with that of the data used in training; adjust with sox if needed. A full example recipe is in egs/tedlium2/align1/.
ESPnet2
CTC segmentation determines utterance segments within audio files. Aligned utterance segments constitute the labels of speech datasets.
As demo, we align start and end of utterances within the audio file ctc_align_test.wav. This can be done either directly from the Python command line or using the script espnet2/bin/asr_align.py.
From the Python command line interface:
# load a model with character tokens
from espnet_model_zoo.downloader import ModelDownloader
d = ModelDownloader(cachedir="./modelcache")
wsjmodel = d.download_and_unpack("kamo-naoyuki/wsj")
# load the example file included in the ESPnet repository
import soundfile
speech, rate = soundfile.read("./test_utils/ctc_align_test.wav")
# CTC segmentation
from espnet2.bin.asr_align import CTCSegmentation
aligner = CTCSegmentation( **wsjmodel , fs=rate )
text = """
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE ON PROPERTY MANAGEMENT
"""
segments = aligner(speech, text)
print(segments)
# utt1 utt 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 utt 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 utt 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 utt 4.20 6.10 -0.4899 AND CONCENTRATE ON PROPERTY MANAGEMENT
Aligning also works with fragments of the text. For this, set the gratis_blank option that allows skipping unrelated audio sections without penalty. It's also possible to omit the utterance names at the beginning of each line, by setting kaldi_style_text to False.
aligner.set_config( gratis_blank=True, kaldi_style_text=False )
text = ["SALE OF THE HOTELS", "PROPERTY MANAGEMENT"]
segments = aligner(speech, text)
print(segments)
# utt_0000 utt 0.37 1.72 -2.0651 SALE OF THE HOTELS
# utt_0001 utt 4.70 6.10 -5.0566 PROPERTY MANAGEMENT
The script espnet2/bin/asr_align.py uses a similar interface. To align utterances:
# ASR model and config files from pretrained model (e.g. from cachedir):
asr_config=<path-to-model>/config.yaml
asr_model=<path-to-model>/valid.*best.pth
# prepare the text file
wav="test_utils/ctc_align_test.wav"
text="test_utils/ctc_align_text.txt"
cat << EOF > ${text}
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE
utt5 ON PROPERTY MANAGEMENT
EOF
# obtain alignments:
python espnet2/bin/asr_align.py --asr_train_config ${asr_config} --asr_model_file ${asr_model} --audio ${wav} --text ${text}
# utt1 ctc_align_test 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 ctc_align_test 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 ctc_align_test 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 ctc_align_test 4.20 4.97 -0.6017 AND CONCENTRATE
# utt5 ctc_align_test 4.97 6.10 -0.3477 ON PROPERTY MANAGEMENT
The output of the script can be redirected to a segments file by adding the argument --output segments. Each line contains file/utterance name, utterance start and end times in seconds and a confidence score; optionally also the utterance text. The confidence score is a probability in log space that indicates how good the utterance was aligned. If needed, remove bad utterances:
min_confidence_score=-7
# here, we assume that the output was written to the file `segments`
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' segments
See the module documentation for more information. It is recommended to use models with RNN-based encoders (such as BLSTMP) for aligning large audio files; rather than using Transformer models that have a high memory consumption on longer audio data. The sample rate of the audio must be consistent with that of the data used in training; adjust with sox if needed.
References
[1] Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, and Tsubasa Ochiai, "ESPnet: End-to-End Speech Processing Toolkit," Proc. Interspeech'18, pp. 2207-2211 (2018)
[2] Suyoun Kim, Takaaki Hori, and Shinji Watanabe, "Joint CTC-attention based end-to-end speech recognition using multi-task learning," Proc. ICASSP'17, pp. 4835--4839 (2017)
[3] Shinji Watanabe, Takaaki Hori, Suyoun Kim, John R. Hershey and Tomoki Hayashi, "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition," IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 8, pp. 1240-1253, Dec. 2017
Citations
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
@inproceedings{inaguma-etal-2020-espnet,
title = "{ESP}net-{ST}: All-in-One Speech Translation Toolkit",
author = "Inaguma, Hirofumi and
Kiyono, Shun and
Duh, Kevin and
Karita, Shigeki and
Yalta, Nelson and
Hayashi, Tomoki and
Watanabe, Shinji",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-demos.34",
pages = "302--311",
}
@inproceedings{li2020espnet,
title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
author={Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph Boeddeker and Zhuo Chen and Shinji Watanabe},
booktitle={Proceedings of IEEE Spoken Language Technology Workshop (SLT)},
pages={785--792},
year={2021},
organization={IEEE},
}
![[not-for-merge] Transformer](https://avatars.githubusercontent.com/u/11988996?v=4)
![[ESPnet2] Transducer](https://avatars.githubusercontent.com/u/41155456?v=4)
![[ESPnet2] distributed training](https://avatars.githubusercontent.com/u/19261024?v=4)
![[WIP] EURO uasr scripts](https://avatars.githubusercontent.com/u/25757051?v=4)
![[WIP] Add S4 decoder in ESPnet2](https://avatars.githubusercontent.com/u/29270985?v=4)
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