README.md

---
tags:
- mms
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
license: cc-by-nc-4.0
datasets:
- google/fleurs
metrics:
- acc
---

# 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 512 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 512 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 512 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-512"

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 512 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>

- 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
- kmr
- nob
- uzb
- snd
- lat
- nya
- grn
- mya
- orm
- lin
- hye
- yue
- pan
- jpn
- kaz
- npi
- kik
- kat
- guj
- kan
- tgk
- ukr
- ces
- lav
- bak
- khm
- cak
- fao
- glg
- ltz
- xog
- lao
- mlt
- sin
- aka
- sna
- che
- mam
- ita
- quc
- srp
- mri
- tuv
- nno
- pus
- eus
- kbp
- ory
- lug
- bre
- luo
- nhx
- slk
- ewe
- fin
- rif
- dan
- yid
- yao
- mos
- quh
- hne
- xon
- new
- quy
- est
- dyu
- ttq
- bam
- pse
- uig
- sck
- ngl
- tso
- mup
- dga
- seh
- lis
- wal
- ctg
- bfz
- bxk
- ceb
- kru
- war
- khg
- bbc
- thl
- vmw
- zne
- sid
- tpi
- nym
- bgq
- bfy
- hlb
- teo
- fon
- kfx
- bfa
- mag
- ayr
- any
- mnk
- adx
- ava
- hyw
- san
- kek
- chv
- kri
- btx
- nhy
- dnj
- lon
- men
- ium
- nga
- nsu
- prk
- kir
- bom
- run
- hwc
- mnw
- ubl
- kin
- rkt
- xmm
- iba
- gux
- ses
- wsg
- tir
- gbm
- mai
- nyy
- nan
- nyn
- gog
- ngu
- hoc
- nyf
- sus
- bcc
- hak
- grt
- suk
- nij
- kaa
- bem
- rmy
- nus
- ach
- awa
- dip
- rim
- nhe
- pcm
- kde
- tem
- quz
- bba
- kbr
- taj
- dik
- dgo
- bgc
- xnr
- kac
- laj
- dag
- ktb
- mgh
- shn
- oci
- zyb
- alz
- wol
- guw
- nia
- bci
- sba
- kab
- nnb
- ilo
- mfe
- xpe
- bcl
- haw
- mad
- ljp
- gmv
- nyo
- kxm
- nod
- sag
- sas
- myx
- sgw
- mak
- kfy
- jam
- lgg
- nhi
- mey
- sgj
- hay
- pam
- heh
- nhw
- yua
- shi
- mrw
- hil
- pag
- cce
- npl
- ace
- kam
- min
- pko
- toi
- ncj
- umb
- hno
- ban
- syl
- bxg
- nse
- xho
- mkw
- nch
- mas
- bum
- mww
- epo
- tzm
- zul
- lrc
- ibo
- abk
- azz
- guz
- ksw
- lus
- ckb
- mer
- pov
- rhg
- knc
- tum
- nso
- bho
- ndc
- ijc
- qug
- lub
- srr
- mni
- zza
- dje
- tiv
- gle
- lua
- swk
- ada
- lic
- skr
- mfa
- bto
- unr
- hdy
- kea
- glk
- ast
- nup
- sat
- ktu
- bhb
- sgc
- dks
- ncl
- emk
- urh
- tsc
- idu
- igb
- its
- kng
- kmb
- tsn
- bin
- gom
- ven
- sef
- sco
- trp
- glv
- haq
- kha
- rmn
- sot
- sou
- gno
- igl
- efi
- nde
- rki
- kjg
- fan
- wci
- bjn
- pmy
- bqi
- ina
- hni
- the
- nuz
- ajg
- ymm
- fmu
- nyk
- snk
- esg
- thq
- pht
- wes
- pnb
- phr
- mui
- tkt
- bug
- mrr
- kas
- zgb
- lir
- vah
- ssw
- iii
- brx
- rwr
- kmc
- dib
- pcc
- zyn
- hea
- hms
- thr
- wbr
- bfb
- wtm
- blk
- dhd
- swv
- zzj
- niq
- mtr
- gju
- kjp
- haz
- shy
- nbl
- aii
- sjp
- bns
- brh
- msi
- tsg
- tcy
- kbl
- noe
- tyz
- ahr
- aar
- wuu
- kbd
- bca
- pwr
- hsn
- kua
- tdd
- bgp
- abs
- zlj
- ebo
- bra
- nhp
- tts
- zyj
- lmn
- cqd
- dcc
- cjk
- bfr
- bew
- arg
- drs
- chw
- bej
- bjj
- ibb
- tig
- nut
- jax
- tdg
- nlv
- pch
- fvr
- mlq
- kfr
- nhn
- tji
- hoj
- cpx
- cdo
- bgn
- btm
- trf
- daq
- max
- nba
- mut
- hnd
- ryu
- abr
- sop
- odk
- nap
- gbr
- czh
- vls
- gdx
- yaf
- sdh
- anw
- ttj
- nhg
- cgg
- ifm
- mdh
- scn
- lki
- luz
- stv
- kmz
- nds
- mtq
- knn
- mnp
- bar
- mzn
- gsw
- fry

</details>

## Model details

- **Developed by:** Vineel Pratap et al.
- **Model type:** Multi-Lingual Automatic Speech Recognition model
- **Language(s):** 512 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)