espnet/owsm_ctc_v4_1B

OWSM-CTC (Peng et al., ACL 2024) is an encoder-only speech foundation model based on hierarchical multi-task self-conditioned CTC. It follows the design of the project, Open Whisper-style Speech Model (OWSM).

OWSM-CTC v4 is trained for three epochs on 320k hours of public audio data covering multilingual speech recognition, any-to-any speech translation, and language identification. The newly curated data will be publicly released. Please stay tuned!

To use the pre-trained model, please install espnet and espnet_model_zoo. The requirements are:

librosa
torch
espnet
espnet_model_zoo

The recipe can be found in ESPnet: https://github.com/espnet/espnet/tree/master/egs2/owsm_ctc_v3.1/s2t1

Example script for batched inference

Speech2TextGreedySearch now provides a unified batched inference method batch_decode. It performs CTC greedy decoding for a batch of short-form or long-form audios. If an audio is shorter than 30s, it will be padded to 30s; otherwise it will be split into overlapped segments (same as the "long-form ASR/ST" method below).

from espnet2.bin.s2t_inference_ctc import Speech2TextGreedySearch

s2t = Speech2TextGreedySearch.from_pretrained(
    "espnet/owsm_ctc_v4_1B",
    device="cuda",
    use_flash_attn=False,   # set to True for better efficiency if flash attn is installed and dtype is float16 or bfloat16
    lang_sym='<eng>',
    task_sym='<asr>',
)

res = s2t.batch_decode(
    "audio.wav",    # a single audio (path or 1-D array/tensor) as input
    batch_size=16,
    context_len_in_secs=4,
)   # res is a single str, i.e., the predicted text without special tokens

res = s2t.batch_decode(
    ["audio1.wav", "audio2.wav", "audio3.wav"], # a list of audios as input
    batch_size=16,
    context_len_in_secs=4,
)   # res is a list of str

# Please check the code of `batch_decode` for all supported inputs

Example script for short-form ASR/ST/LID

Our models are trained on 16kHz audio with a fixed duration of 30s. When using the pre-trained model, please ensure the input speech is 16kHz and pad or truncate it to 30s.

import librosa
from espnet2.bin.s2t_inference_ctc import Speech2TextGreedySearch

s2t = Speech2TextGreedySearch.from_pretrained(
    "espnet/owsm_ctc_v4_1B",
    device="cuda",
    generate_interctc_outputs=False,
    lang_sym='<eng>',
    task_sym='<asr>',
)

# NOTE: OWSM-CTC is trained on 16kHz audio with a fixed 30s duration. Please ensure your input has the correct sample rate; otherwise resample it to 16k before feeding it to the model
speech, rate = librosa.load("xxx.wav", sr=16000)
speech = librosa.util.fix_length(speech, size=(16000 * 30))

res = s2t(speech)[0]
print(res)

Example script for long-form ASR/ST

import soundfile as sf
import torch
from espnet2.bin.s2t_inference_ctc import Speech2TextGreedySearch

context_len_in_secs = 4   # left and right context when doing buffered inference
batch_size = 32   # depends on the GPU memory
s2t = Speech2TextGreedySearch.from_pretrained(
    "espnet/owsm_ctc_v4_1B",
    device='cuda' if torch.cuda.is_available() else 'cpu',
    generate_interctc_outputs=False,
    lang_sym='<eng>',
    task_sym='<asr>',
)

speech, rate = sf.read(
    "xxx.wav"
)

text = s2t.decode_long_batched_buffered(
    speech,
    batch_size=batch_size,
    context_len_in_secs=context_len_in_secs,
)
print(text)

Example of CTC forced alignment using ctc-segmentation

CTC segmentation can be efficiently applied to audio of an arbitrary length.

import soundfile as sf
from espnet2.bin.s2t_ctc_align import CTCSegmentation
from espnet_model_zoo.downloader import ModelDownloader

# Download model first
d = ModelDownloader()
downloaded = d.download_and_unpack("espnet/owsm_ctc_v4_1B")

aligner = CTCSegmentation(
    **downloaded,
    fs=16000,
    ngpu=1,
    batch_size=32,    # batched parallel decoding; reduce it if your GPU memory is smaller
    kaldi_style_text=True,
    time_stamps="auto",     # "auto" can be more accurate than "fixed" when converting token index to timestamp
    lang_sym="<eng>",
    task_sym="<asr>",
    context_len_in_secs=2,  # left and right context in buffered decoding
)

speech, rate = sf.read(
    "./test_utils/ctc_align_test.wav"
)
print(f"speech duration: {len(speech) / rate : .2f} seconds")
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)

OWSM series

Encoder-decoder OWSM

Name	Size	Hugging Face Repo
OWSM v3.1 base	101M	https://huggingface.co/espnet/owsm_v3.1_ebf_base
OWSM v3.1 small	367M	https://huggingface.co/espnet/owsm_v3.1_ebf_small
OWSM v3.1 medium	1.02B	https://huggingface.co/espnet/owsm_v3.1_ebf
OWSM v3.2 small	367M	https://huggingface.co/espnet/owsm_v3.2
OWSM v4 base	102M	https://huggingface.co/espnet/owsm_v4_base_102M
OWSM v4 small	370M	https://huggingface.co/espnet/owsm_v4_small_370M
OWSM v4 medium	1.02B	https://huggingface.co/espnet/owsm_v4_medium_1B

CTC-based OWSM

Name	Size	Hugging Face Repo
OWSM-CTC v3.1 medium	1.01B	https://huggingface.co/espnet/owsm_ctc_v3.1_1B
OWSM-CTC v3.2 medium	1.01B	https://huggingface.co/espnet/owsm_ctc_v3.2_ft_1B
OWSM-CTC v4 medium	1.01B	https://huggingface.co/espnet/owsm_ctc_v4_1B

Citations

OWSM v4

@inproceedings{owsm-v4,
  title={{OWSM} v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning},
  author={Yifan Peng and Shakeel Muhammad and Yui Sudo and William Chen and Jinchuan Tian and Chyi-Jiunn Lin and Shinji Watanabe},
  booktitle={Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH) (accepted)},
  year={2025},
}

OWSM-CTC

@inproceedings{owsm-ctc,
    title = "{OWSM}-{CTC}: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification",
    author = "Peng, Yifan  and
      Sudo, Yui  and
      Shakeel, Muhammad  and
      Watanabe, Shinji",
    booktitle = "Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)",
    year = "2024",
    month= {8},
    url = "https://aclanthology.org/2024.acl-long.549",
}

OWSM v3.1 and v3.2

@inproceedings{owsm-v32,
  title={On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models},
  author={Jinchuan Tian and Yifan Peng and William Chen and Kwanghee Choi and Karen Livescu and Shinji Watanabe},
  booktitle={Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH)},
  year={2024},
  month={9},
  pdf="https://arxiv.org/pdf/2406.09282"
}
@inproceedings{owsm-v31,
  title={{OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer}},
  author={Yifan Peng and Jinchuan Tian and William Chen and Siddhant Arora and Brian Yan and Yui Sudo and Muhammad Shakeel and Kwanghee Choi and Jiatong Shi and Xuankai Chang and Jee-weon Jung and Shinji Watanabe},
  booktitle={Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH)},
  year={2024},
  month={9},
  pdf="https://arxiv.org/pdf/2401.16658",
}

Initial OWSM (v1, v2, v3)

@inproceedings{owsm,
  title={Reproducing Whisper-Style Training Using An Open-Source Toolkit And Publicly Available Data},
  author={Yifan Peng and Jinchuan Tian and Brian Yan and Dan Berrebbi and Xuankai Chang and Xinjian Li and Jiatong Shi and Siddhant Arora and William Chen and Roshan Sharma and Wangyou Zhang and Yui Sudo and Muhammad Shakeel and Jee-weon Jung and Soumi Maiti and Shinji Watanabe},
  booktitle={Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2023},
  month={12},
  pdf="https://arxiv.org/pdf/2309.13876",
}