End-to-end speech processing toolkit
Documentation ·
Installation ·
Recipes ·
Model Zoo ·
Notebooks ·
Discord
ESPnet is an end-to-end speech processing toolkit built on PyTorch. It covers speech recognition, text-to-speech, speech translation, speech enhancement, speaker diarization, spoken language understanding, spoken language identification, singing voice synthesis, speech language models, and more — with Kaldi-style reproducible recipes from data preparation to evaluation, and hundreds of pretrained models on Hugging Face.
What's new
- ESPnet 202611 — audio quality assessment (
espnet2.bin.aqa_inference, the ARECHO metric model and the URGENT24 recipe), so VERSA can install its supporting models from PyPI; Bagpiper on the command line (espnet describe, espnet render) and in the MCP server; scaled dot-product attention through every ASR encoder, relative-position attention included; F5-TTS; hydra-core and omegaconf are now core dependencies, and ctc-segmentation comes from upstream 1.7.5.
- ESPnet 202610.post2 — the command line has one name a task and two more of them:
espnet phonemize reads the phones with POWSM, espnet align lines text up with the audio it was said in, and asr and tts became transcribe and synthesize (the old names still work); models published before June 2025 load again, after init: chainer was removed from the toolkit.
- ESPnet 202610.post1 — the command line grows
espnet demo (the OWSM browser demo) and --live (the microphone, transcribed as you speak); one Speech2Text now loads either kind of OWSM checkpoint, with best_path() for CTC decoding without a search; espnet/espnet:inference-cpu-latest and -gpu-latest run a published model with nothing installed; three more demo Spaces (TTS, enhancement, speaker verification).
- ESPnet 202610 — one-line inference from the command line (
pip install espnet && espnet asr audio.wav), two OWSM v4 demos as Hugging Face Spaces, a core install without the training stack (training is espnet[train]), batched beam search, PyTorch 2.11-2.14.
- ESPnet 202609 — ESPnet3 complete on
egs3/librispeech_100 at ESPnet2 parity, CI rebuilt on a prebuilt image (compute per run halved), OpenBEATs pretraining, ten new recipes (ASR, TTS, SER, ST, audio SSL), Python 3.12-3.13.
- ESPnet 202604 — Docker-based CI, PyTorch 2.9.1 support, FastSpeech2 inference ~1.9x faster at batch 8, new recipes (Kinyarwanda, Emilia, kosp2e).
- ESPnet 202511 — parallel-processing primitives, refactored inference and evaluation pipeline, expanded SpeechLM support.
Full history: Releases.
Install
# Install PyTorch first: https://pytorch.org/get-started/locally/
pip install espnet # run pretrained models
pip install "espnet[train]" # also train them (Lightning, TensorBoard, W&B, Dask)
pip install "espnet[all]" # training plus every task extra (except sds)
pip install git+https://github.com/espnet/espnet # latest master
| system/pytorch ver. | 2.11.0 | 2.13.0 | 2.14.0 |
|---|
| ubuntu/python3.12/pip | | | |
| ubuntu/python3.13/pip | | | |
| debian12/python3.12/conda | | | |
| windows/python3.12/pip | | | |
| macos/python3.12/pip | | | |
| macos/python3.12/conda | |
Each badge is its workflow's aggregate status on master, where the full grid runs. Coverage is not uniform — some suites run on one pytorch only, and a pull request runs less than master does. What each column covers.
Quick start
pip install espnet
Then pick how you want to call it. Same models, same six tasks.
From the terminal
espnet transcribe audio.wav # detects the language
espnet translate audio.wav --to eng
espnet synthesize "Hello from ESPnet" -o out.wav
espnet enhance noisy.wav -o clean.wav
phonemize and align are there too, and espnet models names the default model of each. Every command but that one takes --model <tag> and --device cuda.
From Python
from espnet2.bin.s2t_inference import Speech2Text
# OWSM-CTC v4: multilingual ASR, translation and language ID in one model
s2t = Speech2Text.from_pretrained("espnet/owsm_ctc_v4_1B")
for start, end, text in s2t.decode_long("audio.wav"): # any length or rate
print(text)
Any model from the ESPnet organization, cached after the first download.
From an agent
pip install "espnet[mcp]"
claude mcp add espnet -- espnet-mcp
An MCP server offering the same six tasks as tools, so Claude, Cursor or another agent calls them itself.
docker run --rm -v "$PWD:/data" -v "$HOME/.cache/huggingface:/cache/huggingface" \
espnet/espnet:inference-cpu-latest asr /data/audio.wav
The second mount keeps the downloaded model between runs. With a GPU, use espnet/espnet:inference-gpu-latest, --gpus all and --device cuda; on a Linux host add --user "$(id -u):$(id -g)". The other images are in docker/.
Train a recipe — every corpus follows the same interface:
cd egs2/librispeech/asr1
./run.sh # data → features → training → scoring
./run.sh --stage 11 --stop_stage 13 # or selected stages
New to ESPnet? Start with egs2/mini_an4/asr1 — it runs end to end in minutes.
Supported tasks
| Task | Template | Highlights |
|---|
| 🗣️ | ASR — speech recognition | asr1, asr2 | Hybrid CTC/attention, Transducer, streaming, Conformer / E-Branchformer, Whisper, SSL front-ends |
| 🌏 | S2T — multilingual multitask | s2t1 | OWSM: open Whisper-style models trained on public data |
| 🔊 | TTS — text-to-speech | tts1, tts2 | Tacotron 2, FastSpeech 2, VITS, JETS, multi-speaker / multilingual |
| 🎤 | SVS — singing voice synthesis | svs1, svs2 | VISinger 1/2, Xiaoice, DiffSinger; merged from Muskits |
Each template ships a corpus-agnostic pipeline; see egs2/README.md for the full list of 200+ corpora recipes.
Why ESPnet
- Reproducible — one
run.sh per corpus, from download to scoring, with published results.
- Unified — the same recipe structure, config format, and trainer across every task above.
- Scalable — DDP, multi-node training, Slurm/MPI, DeepSpeed, sharded training, on-the-fly feature extraction.
- Open — hundreds of pretrained models and demos on Hugging Face, plus W&B and TensorBoard logging.
Demos
Four ways to run a published model — a hosted Space, a notebook, the MCP server an agent calls, and the command line. 🟢 is there today, 🚧 is in review, ❌ is not there yet.
Every Space is built from a directory in this repository, and doc/front_ends.md is how a seventh task gets all four columns.
pip install "espnet[demo]" adds espnet demo, the same app on localhost; espnet transcribe --live reads the microphone.
Beyond the six: a Space and a notebook for speaker verification, notebooks for neural codecs and spoken dialogue, and egs2/TEMPLATE/sds1 for the full spoken dialogue system, which runs locally.
| Demo | | Last run |
|---|
| Spoken dialogue — listen, think, speak | | |
| Speech recognition, in any of 151 languages | | |
| The words appearing as the audio arrives | | |
| Speech translation — the same model, a different task symbol | | |
| Text-to-speech, one voice and then 128 | | |
| Speech enhancement, and what it did to the signal-to-noise | |
Each runs top to bottom on a CPU and pins the release it was checked against. The second badge is that notebook being executed cell by cell every Sunday, so a red one names the demo that broke rather than leaving you to find out by opening it. More, including the CMU course material: espnet/notebook.
Publish your own. Every ESPnet3 recipe can wrap its trained model in a
Gradio app and push it to Hugging Face Spaces — the UI,
the Space README.md and requirements.txt are all generated from
conf/demo.yaml.
cd egs3/librispeech_100/esp2_asr
train=conf/tuning/training_e_branchformer.yaml # the config the model was trained with
python run.py --stages pack_model --training_config $train --inference_config conf/inference.yaml --publication_config conf/publication.yaml # -> exp/.../model_pack
python run.py --stages pack_demo --training_config $train --demo_config conf/demo.yaml # -> demo/
python run.py --stages upload_demo --training_config $train --demo_config conf/demo.yaml # needs `hf auth login`
Run the packed app locally with python demo/app.py.
Learn
Contributing
Contributions, questions, and feature requests are all welcome — open an issue or a pull request. First time here? Read the contribution guide.
ESPnet2 and ESPnet3 configuration differences
| Setting | ESPnet2 | ESPnet3 |
|---|
batch_bins (sequence batching) | Global budget for the whole distributed job; each sequence batch is split across ranks | Per-GPU budget by default; dataloader.train.batch_distribution: global opts into a global budget and splits each batch across ranks. Validation selects its mode under dataloader.valid. |
best_model_criterion | Each criterion is (phase, metric, mode); the number of models to keep is set separately with keep_nbest_models | Each criterion is (monitor, top_k, mode), so the number of checkpoints to keep is specified per criterion (for example, valid/loss, 3, min). |
seed | Set initially and reset to seed + epoch at the start of each epoch | Set once before training with Lightning's seed_everything(..., workers=True); random streams are not reset to seed + epoch by ESPnet3. |
Early stopping (patience) | patience and early_stopping_criterion are trainer options | Configure Lightning's EarlyStopping callback in trainer.callbacks (for example, its monitor, patience, and mode); no early-stopping callback is added by default. |
Existing ESPnet3 configs keep per-GPU behavior when batch_distribution is omitted.
Changing the GPU count in that mode changes the effective global batch budget.
Switching modes does not automatically rescale batch_bins or accumulation.
Global mode requires shared batches with at least world_size items;
unsupported iterator/sharding configurations raise an error.
See the migration guide
and dynamic batching guide
for further guidance.
Details
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.)
- Support spoken language identification recipes (voxlingua107, geolid.)
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: Flash 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:
Demonstration
- Speech recognition with OWSM-CTC
- Hosted demo: OWSM-CTC v4, maintained from
egs2/owsm_ctc_v4/s2t1/demo — recognises and identifies 151 languages, translates into 25 of them, and decodes long-form audio
- Hosted demo: OWSM v4, maintained from
egs2/owsm_v4/s2t1/demo — the same four tasks with the encoder-decoder model, which also takes a text prompt
- Streaming ASR, decoded as the audio arrives
TTS: Text-to-speech
- Architecture
- Tacotron2
- Transformer-TTS
- FastSpeech
- FastSpeech2
- Conformer FastSpeech & FastSpeech2
- VITS
- JETS
- Multi-speaker & multi-language extension
- 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
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
- 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.
LID: Spoken Language Identification
- Architecture
- Frontend: Self-supervised models integrated via S3PRL, such as MMS, XLS-R, etc.
- Encoder: ECAPA-TDNN, xvector, etc.
- Pooling methods: Statistical pooling, attentive statistics pooling, etc.
- Supports AAMSoftmax loss with subcenter regularization and topk penalty
- Optional lang2vec auxiliary targets and conditioning on predicted language features; inference uses speech only.
- Supports category-balanced sampling for imbalanced language datasets
- Supports language embeddings extraction, t-SNE visualization, distributed inference, and cross-dataset evaluation
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
- Tight integration with neural vocoders (the same as TTS)
SSL: Self-supervised Learning
- Support HuBERT Pre-training:
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 on PyTorch
- Flexible front-end processing thanks to omniio and HDF5 support
- Tensorboard-based monitoring
- DeepSpeed-based large-scale training
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
ASR results
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 | 4.1/4.4 | N/A | link |
| CSJ eval1/eval2/eval3 | 4.5/3.3/3.6 | N/A | link |
| GigaSpeech dev/test | N/A | 10.6/10.5 | link |
| HKUST dev | 21.2 | N/A | link |
| 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 | 13.4/7.3 | link |
| TEDLIUM2 dev/test | N/A | 7.3/7.1 | link |
| WSJ dev93/eval92 | 1.1/0.8 | 2.8/1.8 |
If you want to check the results of the other recipes, please check egs2/<name_of_recipe>/asr1/README.md.
SE results
We list results from three different models on WSJ0-2mix, which is one the most widely used benchmark dataset for speech separation.
MT results
| Task | BLEU | Pre-trained model |
|---|
| IWSLT'14 test2014 (De->En) | 32.2 | link |
TTS results
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 pre-trained models via espnet_model_zoo.
You can download pre-trained vocoders via kan-bayashi/ParallelWaveGAN.
TTS 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!
English, Japanese, and Mandarin models are available in the demo.
VC results
- 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
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 |
If you want to check the results of the other recipes, please check egs2/<name_of_recipe>/asr1/RESULTS.md.
CTC Segmentation demo
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.
Import CTCSegmentation from espnet2.bin.asr_align for an ASR model and from espnet2.bin.s2t_align for an OWSM-CTC one:
those are the two entry points, and both run the algorithm in espnet2/bin/ctc_segment.py, which is not instantiated on its own.
For a forced alignment over any CTC head instead, and for what espnet align runs, see espnet2/bin/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 https://github.com/espnet/espnet/issues/4278#issuecomment-1100756463.
Citation
If you use ESPnet in your research, please cite the main paper:
@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={{ESP}net2-{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},
}
@inproceedings{gao2023euro,
title={{EURO: ESP}net 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},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
@inproceedings{peng2023reproducing,
title={Reproducing {W}hisper-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},
booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
pages={1--8},
year={2023},
organization={IEEE}
}
@inproceedings{sharma2023espnet,
title={ESPnet-{SUMM}: Introducing a novel large dataset, toolkit, and a cross-corpora evaluation of speech summarization systems},
author={Sharma, Roshan and Chen, William and Kano, Takatomo and Sharma, Ruchira and Arora, Siddhant and Watanabe, Shinji and Ogawa, Atsunori and Delcroix, Marc and Singh, Rita and Raj, Bhiksha},
booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
pages={1--8},
year={2023},
organization={IEEE}
}
@article{jung2024espnet,
title={{ESPnet-SPK}: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models},
author={Jung, Jee-weon and Zhang, Wangyou and Shi, Jiatong and Aldeneh, Zakaria and Higuchi, Takuya and Theobald, Barry-John and Abdelaziz, Ahmed Hussen and Watanabe, Shinji},
journal={Proc. Interspeech 2024},
year={2024}
}
@inproceedings{yan-etal-2023-espnet,
title = "{ESP}net-{ST}-v2: Multipurpose Spoken Language Translation Toolkit",
author = "Yan, Brian and
Shi, Jiatong and
Tang, Yun and
Inaguma, Hirofumi and
Peng, Yifan and
Dalmia, Siddharth and
Pol{\'a}k, Peter and
Fernandes, Patrick and
Berrebbi, Dan and
Hayashi, Tomoki and
Zhang, Xiaohui and
Ni, Zhaoheng and
Hira, Moto and
Maiti, Soumi and
Pino, Juan and
Watanabe, Shinji",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
year = "2023",
publisher = "Association for Computational Linguistics",
pages = "400--411",
}
@inproceedings{someki2022espnet,
title={{ESPnet-ONNX}: Bridging a gap between research and production},
author={Someki, Masao and Higuchi, Yosuke and Hayashi, Tomoki and Watanabe, Shinji},
booktitle={2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
year={2022},
organization={IEEE}
}
@inproceedings{someki2024espnet,
title={{ESPnet-EZ}: Python-only {ESPnet} for easy fine-tuning and integration},
author={Someki, Masao and Choi, Kwanghee and Arora, Siddhant and Chen, William and Cornell, Samuele and Han, Jionghao and Peng, Yifan and Shi, Jiatong and Srivastav, Vaibhav and Watanabe, Shinji},
booktitle={2024 IEEE Spoken Language Technology Workshop (SLT)},
year={2024},
organization={IEEE}
}
@inproceedings{shi2024espnet,
title={{ESPnet-Codec}: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech},
author={Shi, Jiatong and Tian, Jinchuan and Wu, Yihan and Jung, Jee-weon and Yip, Jia Qi and Masuyama, Yoshiki and Chen, William and Wu, Yuning and Tang, Yuxun and Baali, Massa and Alharhi, Dareen and Zhang, Dong and Deng, Ruifan and Srivastava, Tejes and Wu, Haibin and Liu, Alexander H. and Raj, Bhiksha and Jin, Qin and Song, Ruihua and Watanabe, Shinji},
booktitle={2024 IEEE Spoken Language Technology Workshop (SLT)},
pages={562--569},
year={2024},
organization={IEEE}
}
@inproceedings{tian-etal-2025-espnet,
title={{ESP}net-{S}peech{LM}: An Open Speech Language Model Toolkit},
author={Tian, Jinchuan and Shi, Jiatong and Chen, William and Arora, Siddhant and Masuyama, Yoshiki and Maekaku, Takashi and Wu, Yihan and Peng, Junyi and Bharadwaj, Shikhar and Zhao, Yiwen and Cornell, Samuele and Peng, Yifan and Yue, Xiang and Yang, Chao-Han Huck and Neubig, Graham and Watanabe, Shinji},
booktitle={Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)},
pages={116--124},
year={2025},
publisher={Association for Computational Linguistics}
}
@inproceedings{arora-etal-2025-espnet,
title={{ESP}net-{SDS}: Unified Toolkit and Demo for Spoken Dialogue Systems},
author={Arora, Siddhant and Peng, Yifan and Shi, Jiatong and Tian, Jinchuan and Chen, William and Bharadwaj, Shikhar and Futami, Hayato and Kashiwagi, Yosuke and Tsunoo, Emiru and Shimizu, Shuichiro and Srivastav, Vaibhav and Watanabe, Shinji},
booktitle={Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)},
pages={248--259},
year={2025},
publisher={Association for Computational Linguistics}
}
@inproceedings{bharadwaj2025openbeats,
title={{OpenBEATs}: A Fully Open-Source General-Purpose Audio Encoder},
author={Bharadwaj, Shikhar and Cornell, Samuele and Choi, Kwanghee and Fukayama, Satoru and Shim, Hye-jin and Deshmukh, Soham and Watanabe, Shinji},
booktitle={2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
year={2025},
organization={IEEE}
}
@inproceedings{wang2025geolid,
title={Geolocation-Aware Robust Spoken Language Identification},
author={Qingzheng Wang and Hye-jin Shim and Jiancheng Sun and Shinji Watanabe},
booktitle={2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
year={2025},
organization={IEEE},
url={https://arxiv.org/abs/2508.17148}
}
@inproceedings{someki2026espnet3,
title={{ESPnet3}: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era},
author={Someki, Masao and Polok, Alexander and Carvalho, Carlos and Lin, Chyi-Jiunn and Yang, Da-Hee and Shi, Jiatong and Tian, Jinchuan and Yalta Soplin, Nelson Enrique and Cornell, Samuele and Arora, Siddhant and Teixeira, Francisco and Wang, Wei and Chen, William and Abad, Alberto and Li, Chenda and Watanabe, Shinji and Zhang, Wangyou},
booktitle={Interspeech 2026},
year={2026}
}
