upload readme

README.md

@@ -1,4 +1,555 @@
 ---
 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 256 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 256 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 256 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-256"
+
+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 256 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
+- fao
+- glg
+- ltz
+- xog
+- lao
+- mlt
+- sin
+- aka
+- sna
+- ita
+- srp
+- mri
+- nno
+- pus
+- eus
+- ory
+- lug
+- bre
+- luo
+- slk
+- ewe
+- fin
+- rif
+- dan
+- yid
+- yao
+- mos
+- hne
+- est
+- dyu
+- bam
+- uig
+- sck
+- tso
+- mup
+- ctg
+- ceb
+- war
+- bbc
+- vmw
+- sid
+- tpi
+- mag
+- san
+- kri
+- lon
+- kir
+- run
+- ubl
+- kin
+- rkt
+- xmm
+- tir
+- mai
+- nan
+- nyn
+- bcc
+- hak
+- suk
+- bem
+- rmy
+- awa
+- pcm
+- bgc
+- shn
+- oci
+- wol
+- bci
+- kab
+- ilo
+- bcl
+- haw
+- mad
+- nod
+- sag
+- sas
+- jam
+- mey
+- shi
+- hil
+- ace
+- kam
+- min
+- umb
+- hno
+- ban
+- syl
+- bxg
+- xho
+- mww
+- epo
+- tzm
+- zul
+- ibo
+- abk
+- guz
+- ckb
+- knc
+- nso
+- bho
+- dje
+- tiv
+- gle
+- lua
+- skr
+- bto
+- kea
+- glk
+- ast
+- sat
+- ktu
+- bhb
+- emk
+- kng
+- kmb
+- tsn
+- gom
+- ven
+- sco
+- glv
+- sot
+- sou
+- gno
+- nde
+- bjn
+- ina
+- fmu
+- esg
+- wes
+- pnb
+- phr
+- mui
+- bug
+- mrr
+- kas
+- lir
+- vah
+- ssw
+- rwr
+- pcc
+- hms
+- wbr
+- swv
+- mtr
+- haz
+- aii
+- bns
+- msi
+- wuu
+- hsn
+- bgp
+- tts
+- lmn
+- dcc
+- bew
+- bjj
+- ibb
+- tji
+- hoj
+- cpx
+- cdo
+- daq
+- mut
+- nap
+- czh
+- gdx
+- sdh
+- scn
+- mnp
+- bar
+- mzn
+- gsw
+
+</details>
+
+## Model details
+
+- **Developed by:** Vineel Pratap et al.
+- **Model type:** Multi-Lingual Automatic Speech Recognition model
+- **Language(s):** 256 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)

langs.txt

@@ -0,0 +1,256 @@
+- 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
+- fao
+- glg
+- ltz
+- xog
+- lao
+- mlt
+- sin
+- aka
+- sna
+- ita
+- srp
+- mri
+- nno
+- pus
+- eus
+- ory
+- lug
+- bre
+- luo
+- slk
+- ewe
+- fin
+- rif
+- dan
+- yid
+- yao
+- mos
+- hne
+- est
+- dyu
+- bam
+- uig
+- sck
+- tso
+- mup
+- ctg
+- ceb
+- war
+- bbc
+- vmw
+- sid
+- tpi
+- mag
+- san
+- kri
+- lon
+- kir
+- run
+- ubl
+- kin
+- rkt
+- xmm
+- tir
+- mai
+- nan
+- nyn
+- bcc
+- hak
+- suk
+- bem
+- rmy
+- awa
+- pcm
+- bgc
+- shn
+- oci
+- wol
+- bci
+- kab
+- ilo
+- bcl
+- haw
+- mad
+- nod
+- sag
+- sas
+- jam
+- mey
+- shi
+- hil
+- ace
+- kam
+- min
+- umb
+- hno
+- ban
+- syl
+- bxg
+- xho
+- mww
+- epo
+- tzm
+- zul
+- ibo
+- abk
+- guz
+- ckb
+- knc
+- nso
+- bho
+- dje
+- tiv
+- gle
+- lua
+- skr
+- bto
+- kea
+- glk
+- ast
+- sat
+- ktu
+- bhb
+- emk
+- kng
+- kmb
+- tsn
+- gom
+- ven
+- sco
+- glv
+- sot
+- sou
+- gno
+- nde
+- bjn
+- ina
+- fmu
+- esg
+- wes
+- pnb
+- phr
+- mui
+- bug
+- mrr
+- kas
+- lir
+- vah
+- ssw
+- rwr
+- pcc
+- hms
+- wbr
+- swv
+- mtr
+- haz
+- aii
+- bns
+- msi
+- wuu
+- hsn
+- bgp
+- tts
+- lmn
+- dcc
+- bew
+- bjj
+- ibb
+- tji
+- hoj
+- cpx
+- cdo
+- daq
+- mut
+- nap
+- czh
+- gdx
+- sdh
+- scn
+- mnp
+- bar
+- mzn
+- gsw