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indicnode/
omniASR-CTC-1B

automatic-speech-recognition

facebook/omnilingual-asr-corpus

apache-2.0

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Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages

Model Card for omniASR-CTC-1B

Model Description

This model is part of the Omnilingual ASR family released by Meta AI. The original suite includes:

Model Name	Features	Parameters	Download Size (FP32)	Inference VRAM¹	Real-Time Factor¹ (relative speed)²
`omniASR_W2V_300M`	SSL	317_390_592	1.2 GiB
`omniASR_W2V_1B`	SSL	965_514_752	3.6 GiB
`omniASR_W2V_3B`	SSL	3_064_124_672	12.0 GiB
`omniASR_W2V_7B`	SSL	6_488_487_168	25.0 GiB
`omniASR_CTC_300M`	ASR	325_494_996	1.3 GiB	~2 GiB	0.001 (96x)
`omniASR_CTC_1B`	ASR	975_065_300	3.7 GiB	~3 GiB	0.002 (48x)
`omniASR_CTC_3B`	ASR	3_080_423_636	12.0 GiB	~8 GiB	0.003 (32x)
`omniASR_CTC_7B`	ASR	6_504_786_132	25.0 GiB	~15 GiB	0.006 (16x)
`omniASR_LLM_300M`	ASR with optional language conditioning	1_627_603_584	6.1 GiB	~5 GiB	0.090 (~1x)
`omniASR_LLM_1B`	ASR with optional language conditioning	2_275_710_592	8.5 GiB	~6 GiB	0.091 (~1x)
`omniASR_LLM_3B`	ASR with optional language conditioning	4_376_679_040	17.0 GiB	~10 GiB	0.093 (~1x)
`omniASR_LLM_7B`	ASR with optional language conditioning	7_801_041_536	30.0 GiB	~17 GiB	0.092 (~1x)
`omniASR_LLM_7B_ZS`	Zero-Shot ASR	7_810_900_608	30.0 GiB	~20 GiB	0.194 (~0.5x)

¹ (batch=1, audio_len=30s, BF16, A100)

² Relative speed to omniASR_LLM_7B

Installation

The models were developed using fairseq2, a research-focused sequence modeling toolkit. While we provide a reference inference pipeline that works across platforms, audio support requires libsndfile (Mac: brew install libsndfile; Windows may need an additional setup).

bash

# using pip
pip install omnilingual-asr

# using uv
uv add omnilingual-asr

Inference

python

from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline

pipeline = ASRInferencePipeline(model_card="omniASR_LLM_7B")

audio_files = ["/path/to/eng_audio1.flac", "/path/to/deu_audio2.wav"]
lang = ["eng_Latn", "deu_Latn"]
transcriptions = pipeline.transcribe(audio_files, lang=lang, batch_size=2)

Supported Languages

To view the full list of 1600+ supported languages, you can access the language list programmatically:

python

from omnilingual_asr.models.wav2vec2_llama.lang_ids import supported_langs

# Print all supported languages
print(f"Total supported languages: {len(supported_langs)}")
print(supported_langs)

# Check if a specific language is supported
if "eng_Latn" in supported_langs:
    print("English (Latin script) is supported!")

Languages follow the format {language_code}_{script}, for example eng_Latn - English (Latin script), cmn_Hans - Mandarin Chinese (Simplified), ...

Training

To further finetune the released checkpoints on your own data, use our data preparation guide followed by the finetuning recipe guide.

Citation

BibTeX:

bibtex

@misc{omnilingualasr2025,
  title={{Omnilingual ASR}: Open-Source Multilingual Speech Recognition for 1600+ Languages},
  author={{Omnilingual ASR Team} and Keren, Gil and Kozhevnikov, Artyom and Meng, Yen and Ropers, Christophe and Setzler, Matthew and Wang, Skyler and Adebara, Ife and Auli, Michael and Can, Balioglu and Chan, Kevin and Cheng, Chierh and Chuang, Joe and Droof, Caley and Duppenthaler, Mark and Duquenne, Paul-Ambroise and Erben, Alexander and Gao, Cynthia and Mejia Gonzalez, Gabriel and Lyu, Kehan and Miglani, Sagar and Pratap, Vineel and Sadagopan, Kaushik Ram and Saleem, Safiyyah and Turkatenko, Arina and Ventayol-Boada, Albert and Yong, Zheng-Xin and Chung, Yu-An and Maillard, Jean and Moritz, Rashel and Mourachko, Alexandre and Williamson, Mary and Yates, Shireen},
  year={2025},
  url={https://ai.meta.com/research/publications/omnilingual-asr-open-source-multilingual-speech-recognition-for-1600-languages/},
}

Developed by: Meta AI / Omnilingual ASR Team(GitHub)
Model type: End-to-end automatic speech recognition model (wav2vec2-style encoder with CTC head / encoder-decoder, depending on checkpoint).
Language(s) (NLP): 1,600+ languages overall in Omnilingual ASR; this corpus release specifically covers 348 under-served languages across many writing systems (Latin, Arabic, Devanagari, etc.).(GitHub)
License: Apache-2.0 (for the model and code), CC-BY-4.0 for the facebook/omnilingual-asr-corpus dataset.(GitHub)

indicnode/omniASR-CTC-1B