--- title: mms-lid-126 canonical_url: "https://www.modelscope.cn/models/facebook/mms-lid-126" md_url: "https://www.modelscope.cn/models/facebook/mms-lid-126.md" repository: facebook/mms-lid-126 last_updated: 2025-05-20 license: cc-by-nc-4.0 pipeline_tag: audio-classification tasks: - audio-classification model_type: - wav2vec2 architectures: - Wav2Vec2ForSequenceClassification parameters: 966.1M tensor_type: - F32 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - no - no - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za downloads: 508 stars: 0 tags: - mms --- # mms-lid-126 > mms-lid-126 - facebook 在 ModelScope 开源的模型。Massively Multilingual Speech (MMS) - Finetuned LID facebook/mms-lid-126 是 ModelScope 魔搭社区上的 966.1M 参数audio-classification模型，采用 cc-by-nc-4.0 许可。 - **Repository**: facebook/mms-lid-126 - **License**: cc-by-nc-4.0 - **Tasks**: audio-classification - **Parameters**: 966.1M - **Tags**: mms - **Downloads**: 508 - **Stars**: 0 - **Last updated**: 2025-05-20 Source: https://www.modelscope.cn/models/facebook/mms-lid-126 --- # Massively Multilingual Speech (MMS) - Finetuned LID This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 126 output classes (each class representing a language). The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 126 languages. ## Table Of Content - [Example](#example) - [Supported Languages](#supported-languages) - [Model details](#model-details) - [Additional links](#additional-links) ## Example This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify the spoken language of an audio. It can recognize the [following 126 languages](#supported-languages). Let's look at a simple example. First, we install transformers and some other libraries ``` pip install torch accelerate torchaudio datasets pip install --upgrade transformers ```` **Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from source: ``` pip install git+https://github.com/huggingface/transformers.git ``` Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Arabic stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) ar_sample = next(iter(stream_data))["audio"]["array"] ``` Next, we load the model and processor ```py from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor import torch model_id = "facebook/mms-lid-126" processor = AutoFeatureExtractor.from_pretrained(model_id) model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) ``` Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) ```py # English inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'eng' # Arabic inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits lang_id = torch.argmax(outputs, dim=-1)[0].item() detected_lang = model.config.id2label[lang_id] # 'ara' ``` To see all the supported languages of a checkpoint, you can print out the language ids as follows: ```py processor.id2label.values() ``` For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). ## Supported Languages This model supports 126 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html).
Click to toggle - ara - cmn - eng - spa - fra - mlg - swe - por - vie - ful - sun - asm - ben - zlm - kor - ind - hin - tuk - urd - aze - slv - mon - hau - tel - swh - bod - rus - tur - heb - mar - som - tgl - tat - tha - cat - ron - mal - bel - pol - yor - nld - bul - hat - afr - isl - amh - tam - hun - hrv - lit - cym - fas - mkd - ell - bos - deu - sqi - jav - nob - uzb - snd - lat - nya - grn - mya - orm - lin - hye - yue - pan - jpn - kaz - npi - kat - guj - kan - tgk - ukr - ces - lav - bak - khm - fao - glg - ltz - lao - mlt - sin - sna - ita - srp - mri - nno - pus - eus - ory - lug - bre - luo - slk - fin - dan - yid - est - ceb - war - san - kir - oci - wol - haw - kam - umb - xho - epo - zul - ibo - abk - ckb - nso - gle - kea - ast - sco - glv - ina
## Model details - **Developed by:** Vineel Pratap et al. - **Model type:** Multi-Lingual Automatic Speech Recognition model - **Language(s):** 126 languages, see [supported languages](#supported-languages) - **License:** CC-BY-NC 4.0 license - **Num parameters**: 1 billion - **Audio sampling rate**: 16,000 kHz - **Cite as:** @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ## Additional Links - [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) - [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). - [Paper](https://arxiv.org/abs/2305.13516) - [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) - [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) - MMS base checkpoints: - [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) - [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) - [Official Space](https://huggingface.co/spaces/facebook/MMS)