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README.md

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 - mms
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+language:
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+- ar
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+- bm
+- be
+- bn
+- bi
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+- br
+- bg
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+- cs
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+- cv
+- ku
+- cy
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+- ig
+- ia
+- ms
+- is
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+- my
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+- nl
+- 'no'
+- 'no'
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+- oc
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+- or
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+- pt
+- ms
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+- uk
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+- xh
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+- za
+license: cc-by-nc-4.0
+datasets:
+- google/fleurs
+metrics:
+- acc
+---
+
+# Massively Multilingual Speech (MMS) - Finetuned LID - ALL
+
+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 makes use of adapter models to transcribe 1000+ languages.
+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 Wav2Vec2ForCTC, AutoFeatureExtractor
+import torch
+
+model_id = "facebook/mms-lid-126"
+
+processor = AutoFeatureExtractor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.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).
+<details>
+  <summary>Click to toggle</summary>
+
+</details>
+
+## Model details
+
+- **Developed by:** Vineel Pratap et al.
+- **Model type:** Multi-Lingual Automatic Speech Recognition model
+- **Language(s):** 1000+ 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)