ESPnet: end-to-end speech processing toolkit

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ESPnet is an end-to-end speech processing toolkit covering end-to-end speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, and so on. ESPnet uses pytorch as a deep learning engine and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for various speech processing experiments.

Tutorial Series

2019 Tutorial at Interspeech
- Material
2021 Tutorial at CMU
- Online video
- Material
2022 Tutorial at CMU
- Usage of ESPnet (ASR as an example)
  - Online video
  - Material
- Add new models/tasks to ESPnet
  - Online video
  - Material

Key Features

Kaldi-style complete recipe

Support numbers of ASR recipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, Gigaspeech, etc.)
Support numbers of TTS recipes in a similar manner to the ASR recipe (LJSpeech, LibriTTS, M-AILABS, etc.)
Support numbers of ST recipes (Fisher-CallHome Spanish, Libri-trans, IWSLT'18, How2, Must-C, Mboshi-French, etc.)
Support numbers of MT recipes (IWSLT'14, IWSLT'16, the above ST recipes etc.)
Support numbers of SLU recipes (CATSLU-MAPS, FSC, Grabo, IEMOCAP, JDCINAL, SNIPS, SLURP, SWBD-DA, etc.)
Support numbers of SE/SS recipes (DNS-IS2020, LibriMix, SMS-WSJ, VCTK-noisyreverb, WHAM!, WHAMR!, WSJ-2mix, etc.)
Support voice conversion recipe (VCC2020 baseline)
Support speaker diarization recipe (mini_librispeech, librimix)
Support singing voice synthesis recipe (ofuton_p_utagoe_db, opencpop, m4singer, etc.)

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), Transformer, Conformer, Branchformer, or E-Branchformer
- Decoder: RNN (LSTM/GRU), Transformer, or S4
Attention: Dot product, location-aware attention, variants of multi-head
Incorporate RNNLM/LSTMLM/TransformerLM/N-gram trained only with text data
Batch GPU decoding
Data augmentation
Transducer based end-to-end ASR
- Architecture:
  - Custom encoder supporting RNNs, Conformer, Branchformer (w/ variants), 1D Conv / TDNN.
  - Decoder w/ parameters shared across blocks supporting RNN, stateless w/ 1D Conv, MEGA, and RWKV.
  - Pre-encoder: VGG2L or Conv2D available.
- Search algorithms:
  - Greedy search constrained to one emission by timestep.
  - Default beam search algorithm [Graves, 2012] 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:
  - Unified interface for offline and streaming speech recognition.
  - Multi-task learning with various auxiliary losses:
    - Encoder: CTC, auxiliary Transducer and symmetric KL divergence.
    - Decoder: cross-entropy w/ label smoothing.
  - Transfer learning with an acoustic model and/or language model.
  - Training with FastEmit regularization method [Yu et al., 2021].
Please refer to the tutorial page for complete documentation.
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 pre-trained model as Encoder, imported from FairSeq.
Self-supervised learning representations as features, using upstream models in S3PRL in frontend.
- Set frontend to s3prl
- Select any upstream model by setting the frontend_conf to the corresponding name.
Transfer Learning :
- easy usage and transfers from models previously trained by your group or models from ESPnet Hugging Face repository.
- Documentation and toy example runnable on colab.
Streaming Transformer/Conformer ASR with blockwise synchronous beam search.
Restricted Self-Attention based on Longformer as an encoder for long sequences
OpenAI Whisper model, robust ASR based on large-scale, weakly-supervised multitask learning

Demonstration

Real-time ASR demo with ESPnet2
Gradio Web Demo on Hugging Face Spaces. Check out the Web Demo
Streaming Transformer ASR Local Demo with ESPnet2.

TTS: Text-to-speech

Architecture
- Tacotron2
- Transformer-TTS
- FastSpeech
- FastSpeech2
- Conformer FastSpeech & FastSpeech2
- VITS
- JETS
Multi-speaker & multi-language extention
- Pre-trained 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, JETS, etc.)
- 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
Integrated to Hugging Face Spaces with Gradio. See demo:

To train the neural vocoder, please check the following repositories:

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, TasNet, DPRNN, SkiM, SVoice, DC-CRN, DCCRN, Deep Clustering, Deep Attractor Network, FaSNet, iFaSNet, Neural Beamformers, etc.
Flexible ASR integration: working as an individual task or as the ASR frontend
Easy to import pre-trained models from Asteroid
- Both the pre-trained models from Asteroid and the specific configuration are supported.

Demonstration

Interactive SE demo with ESPnet2
Streaming 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 Mel spectrogram
End-to-end VC based on cascaded ASR+TTS (Baseline system for Voice Conversion Challenge 2020!)

SLU: Spoken Language Understanding

Architecture
- Transformer-based Encoder
- Conformer-based Encoder
- Branchformer based Encoder
- E-Branchformer based Encoder
- RNN based Decoder
- Transformer-based Decoder
Support Multitasking with ASR
- Predict both intent and ASR transcript
Support Multitasking with NLU
- Deliberation encoder based 2 pass model
Support using pre-trained ASR models
- Hubert
- Wav2vec2
- VQ-APC
- TERA and more ...
Support using pre-trained NLP models
- BERT
- MPNet And more...
Various language support
- En / Jp / Zn / Nl / And more...
Supports using context from previous utterances
Supports using other tasks like SE in a pipeline manner
Supports Two Pass SLU that combines audio and ASR transcript Demonstration
Performing noisy spoken language understanding using a speech enhancement model followed by a spoken language understanding model.
Performing two-pass spoken language understanding where the second pass model attends to both acoustic and semantic information.
Integrated to Hugging Face Spaces with Gradio. See SLU demo on multiple languages:

SUM: Speech Summarization

End to End Speech Summarization Recipe for Instructional Videos using Restricted Self-Attention [Sharma et al., 2022]

SVS: Singing Voice Synthesis

Framework merge from Muskits
Architecture
- RNN-based non-autoregressive model
- Xiaoice
- Tacotron-singing
- DiffSinger (in progress)
- VISinger
- VISinger 2 (its variations with different vocoders-architecture)
Support multi-speaker & multilingual singing synthesis
- Speaker ID embedding
- Language ID embedding
Various language support
- Jp / En / Kr / Zh
Tight integration with neural vocoders (the same as TTS)

SSL: Self-supervised Learning

Support HuBERT Pre-training:
- Example recipe: egs2/LibriSpeech/ssl1

UASR: Unsupervised ASR (EURO: ESPnet Unsupervised Recognition - Open-source)

Architecture
- wav2vec-U (with different self-supervised models)
- wav2vec-U 2.0 (in progress)
Support PrefixBeamSearch and K2-based WFST decoding

S2T: Speech-to-text with Whisper-style multilingual multitask models

Reproduces Whisper-style training from scratch using public data: OWSM
Supports multiple tasks in a single model
- Multilingual speech recognition
- Any-to-any speech translation
- Language identification
- Utterance-level timestamp prediction (segmentation)

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 that can be applied to 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:

# We recommend you install PyTorch before installing espnet following https://pytorch.org/get-started/locally/
pip install espnet
# To install the latest
# pip install git+https://github.com/espnet/espnet
# To install additional packages
# pip install "espnet[all]"

If you use ESPnet1, please install chainer and cupy.

pip install chainer==6.0.0 cupy==6.0.0    # [Option]

You might need to install some packages depending on each task. We prepared various installation scripts at tools/installers.

(ESPnet2) Once installed, run wandb login and set --use_wandb true to enable tracking runs using W&B.

Docker Container

go to docker/ and follow instructions.

Contribution

Thank you for taking the time for ESPnet! Any contributions to ESPnet are welcome, and feel free to ask any questions or requests to issues. If it's your first ESPnet contribution, please follow the contribution guide.

ASR results

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We list the character error rate (CER) and word error rate (WER) of major ASR tasks.

Task	CER (%)	WER (%)	Pre-trained model
Aishell dev/test	4.6/5.1	N/A	link
ESPnet2 Aishell dev/test	4.1/4.4	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
ESPnet2 GigaSpeech dev/test	N/A	10.6/10.5	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
ESPnet2 Switchboard (eval2000) callhm/swbd	N/A	13.4/7.3	link
TEDLIUM2 dev/test	N/A	8.6/7.2	link
ESPnet2 TEDLIUM2 dev/test	N/A	7.3/7.1	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

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You can recognize speech in a WAV file using pre-trained models. Go to a recipe directory and run utils/recog_wav.sh as follows:

# go to the 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 pre-trained models in the demo script are listed 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

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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

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You can try the interactive demo with Google Colab. Please click the following button to get access to the demos.

It is based on ESPnet2. Pre-trained models are available for both speech enhancement and speech separation tasks.

Speech separation streaming demos:

ST results

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We list 4-gram BLEU of major ST tasks.

end-to-end system

Task	BLEU	Pre-trained 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	Pre-trained 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 pre-trained 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 pre-trained 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

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Task	BLEU	Pre-trained 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'14 test2014 (De->En)	32.2	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.

ESPnet2 TTS generated samples

Note that in the generation, we use Griffin-Lim (wav/) and Parallel WaveGAN (wav_pwg/).

You can download pre-trained models via espnet_model_zoo.

You can download pre-trained 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:

You can download all of the pre-trained 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 the 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 pre-trained 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 pre-trained 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 a shell script to perform synthesis. 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 an 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 pre-trained model as follows:

synth_wav.sh --models ljspeech.fastspeech.v1 example.txt

Waveform synthesis is performed with the Griffin-Lim algorithm and neural vocoders (WaveNet and ParallelWaveGAN). You can change the pre-trained 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

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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

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We list the performance on various SLU tasks and datasets using the metric reported in the original dataset paper

Task	Dataset	Metric	Result	Pre-trained Model
Intent Classification	SLURP	Acc	86.3	link
Intent Classification	FSC	Acc	99.6	link
Intent Classification	FSC Unseen Speaker Set	Acc	98.6	link
Intent Classification	FSC Unseen Utterance Set	Acc	86.4	link
Intent Classification	FSC Challenge Speaker Set	Acc	97.5	link
Intent Classification	FSC Challenge Utterance Set	Acc	78.5	link
Intent Classification	SNIPS	F1	91.7	link
Intent Classification	Grabo (Nl)	Acc	97.2	link
Intent Classification	CAT SLU MAP (Zn)	Acc	78.9	link
Intent Classification	Google Speech Commands	Acc	98.4	link
Slot Filling	SLURP	SLU-F1	71.9	link
Dialogue Act Classification	Switchboard	Acc	67.5	link
Dialogue Act Classification	Jdcinal (Jp)	Acc	67.4	link
Emotion Recognition	IEMOCAP	Acc	69.4	link
Emotion Recognition	swbd_sentiment	Macro F1	61.4	link
Emotion Recognition	slue_voxceleb	Macro F1	44.0	link

If you want to check the results of the other recipes, please check egs2/<name_of_recipe>/asr1/RESULTS.md.

CTC Segmentation demo

ESPnet1

CTC segmentation determines utterance segments within audio files. Aligned utterance segments constitute the labels of speech datasets.

As a demo, we align the 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 names, utterance start and end times in seconds, and a confidence score. The confidence score is a probability in log space that indicates how well 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 pre-trained ASR model (see the 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 with 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 a demo, we align the 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 pre-trained 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 the 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 well 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.

Also, we can use this tool to provide token-level segmentation information if we prepare a list of tokens instead of that of utterances in the text file. See the discussion in espnet#4278 (comment).

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",
}
@article{hayashi2021espnet2,
  title={Espnet2-tts: Extending the edge of tts research},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Yoshimura, Takenori and Wu, Peter and Shi, Jiatong and Saeki, Takaaki and Ju, Yooncheol and Yasuda, Yusuke and Takamichi, Shinnosuke and Watanabe, Shinji},
  journal={arXiv preprint arXiv:2110.07840},
  year={2021}
}
@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},
}
@inproceedings{arora2021espnet,
  title={{ESPnet-SLU}: Advancing Spoken Language Understanding through ESPnet},
  author={Arora, Siddhant and Dalmia, Siddharth and Denisov, Pavel and Chang, Xuankai and Ueda, Yushi and Peng, Yifan and Zhang, Yuekai and Kumar, Sujay and Ganesan, Karthik and Yan, Brian and others},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7167--7171},
  year={2022},
  organization={IEEE}
}
@inproceedings{shi2022muskits,
  author={Shi, Jiatong and Guo, Shuai and Qian, Tao and Huo, Nan and Hayashi, Tomoki and Wu, Yuning and Xu, Frank and Chang, Xuankai and Li, Huazhe and Wu, Peter and Watanabe, Shinji and Jin, Qin},
  title={{Muskits}: an End-to-End Music Processing Toolkit for Singing Voice Synthesis},
  year={2022},
  booktitle={Proceedings of Interspeech},
  pages={4277-4281},
  url={https://www.isca-speech.org/archive/pdfs/interspeech_2022/shi22d_interspeech.pdf}
}
@inproceedings{lu22c_interspeech,
  author={Yen-Ju Lu and Xuankai Chang and Chenda Li and Wangyou Zhang and Samuele Cornell and Zhaoheng Ni and Yoshiki Masuyama and Brian Yan and Robin Scheibler and Zhong-Qiu Wang and Yu Tsao and Yanmin Qian and Shinji Watanabe},
  title={{ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={5458--5462},
}
@article{gao2022euro,
  title={{EURO}: {ESPnet} Unsupervised ASR Open-source Toolkit},
  author={Gao, Dongji and Shi, Jiatong and Chuang, Shun-Po and Garcia, Leibny Paola and Lee, Hung-yi and Watanabe, Shinji and Khudanpur, Sanjeev},
  journal={arXiv preprint arXiv:2211.17196},
  year={2022}
}
@article{peng2023reproducing,
  title={Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data},
  author={Peng, Yifan and Tian, Jinchuan and Yan, Brian and Berrebbi, Dan and Chang, Xuankai and Li, Xinjian and Shi, Jiatong and Arora, Siddhant and Chen, William and Sharma, Roshan and others},
  journal={arXiv preprint arXiv:2309.13876},
  year={2023}
}

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ESPnet: end-to-end speech processing toolkit

Tutorial Series

Key Features

Kaldi-style complete recipe

ASR: Automatic Speech Recognition

TTS: Text-to-speech

SE: Speech enhancement (and separation)

ST: Speech Translation & MT: Machine Translation

VC: Voice conversion

SLU: Spoken Language Understanding

SUM: Speech Summarization

SVS: Singing Voice Synthesis

SSL: Self-supervised Learning

UASR: Unsupervised ASR (EURO: ESPnet Unsupervised Recognition - Open-source)

S2T: Speech-to-text with Whisper-style multilingual multitask models

DNN Framework

ESPnet2

Installation

Docker Container

Contribution

ASR results

ASR demo

SE results

SE demos

ST results

end-to-end system

cascaded system

ST demo

MT results

TTS results

TTS demo

VC results

SLU results

CTC Segmentation demo

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