Duplicate from facebook/mms-lid-256

Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>

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README.md

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+---
+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)

config.json

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+{
+  "_name_or_path": "mms-lid-256",
+  "activation_dropout": 0.05,
+  "adapter_attn_dim": 16,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForSequenceClassification"
+  ],
+  "attention_dropout": 0.05,
+  "bos_token_id": 1,
+  "classifier_proj_size": 1024,
+  "codevector_dim": 1024,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.05,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.05,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.05,
+  "hidden_size": 1280,
+  "id2label": {
+    "0": "ara",
+    "1": "cmn",
+    "2": "eng",
+    "3": "spa",
+    "4": "fra",
+    "5": "mlg",
+    "6": "swe",
+    "7": "por",
+    "8": "vie",
+    "9": "ful",
+    "10": "sun",
+    "11": "asm",
+    "12": "ben",
+    "13": "zlm",
+    "14": "kor",
+    "15": "ind",
+    "16": "hin",
+    "17": "tuk",
+    "18": "urd",
+    "19": "aze",
+    "20": "slv",
+    "21": "mon",
+    "22": "hau",
+    "23": "tel",
+    "24": "swh",
+    "25": "bod",
+    "26": "rus",
+    "27": "tur",
+    "28": "heb",
+    "29": "mar",
+    "30": "som",
+    "31": "tgl",
+    "32": "tat",
+    "33": "tha",
+    "34": "cat",
+    "35": "ron",
+    "36": "mal",
+    "37": "bel",
+    "38": "pol",
+    "39": "yor",
+    "40": "nld",
+    "41": "bul",
+    "42": "hat",
+    "43": "afr",
+    "44": "isl",
+    "45": "amh",
+    "46": "tam",
+    "47": "hun",
+    "48": "hrv",
+    "49": "lit",
+    "50": "cym",
+    "51": "fas",
+    "52": "mkd",
+    "53": "ell",
+    "54": "bos",
+    "55": "deu",
+    "56": "sqi",
+    "57": "jav",
+    "58": "kmr",
+    "59": "nob",
+    "60": "uzb",
+    "61": "snd",
+    "62": "lat",
+    "63": "nya",
+    "64": "grn",
+    "65": "mya",
+    "66": "orm",
+    "67": "lin",
+    "68": "hye",
+    "69": "yue",
+    "70": "pan",
+    "71": "jpn",
+    "72": "kaz",
+    "73": "npi",
+    "74": "kik",
+    "75": "kat",
+    "76": "guj",
+    "77": "kan",
+    "78": "tgk",
+    "79": "ukr",
+    "80": "ces",
+    "81": "lav",
+    "82": "bak",
+    "83": "khm",
+    "84": "fao",
+    "85": "glg",
+    "86": "ltz",
+    "87": "xog",
+    "88": "lao",
+    "89": "mlt",
+    "90": "sin",
+    "91": "aka",
+    "92": "sna",
+    "93": "ita",
+    "94": "srp",
+    "95": "mri",
+    "96": "nno",
+    "97": "pus",
+    "98": "eus",
+    "99": "ory",
+    "100": "lug",
+    "101": "bre",
+    "102": "luo",
+    "103": "slk",
+    "104": "ewe",
+    "105": "fin",
+    "106": "rif",
+    "107": "dan",
+    "108": "yid",
+    "109": "yao",
+    "110": "mos",
+    "111": "hne",
+    "112": "est",
+    "113": "dyu",
+    "114": "bam",
+    "115": "uig",
+    "116": "sck",
+    "117": "tso",
+    "118": "mup",
+    "119": "ctg",
+    "120": "ceb",
+    "121": "war",
+    "122": "bbc",
+    "123": "vmw",
+    "124": "sid",
+    "125": "tpi",
+    "126": "mag",
+    "127": "san",
+    "128": "kri",
+    "129": "lon",
+    "130": "kir",
+    "131": "run",
+    "132": "ubl",
+    "133": "kin",
+    "134": "rkt",
+    "135": "xmm",
+    "136": "tir",
+    "137": "mai",
+    "138": "nan",
+    "139": "nyn",
+    "140": "bcc",
+    "141": "hak",
+    "142": "suk",
+    "143": "bem",
+    "144": "rmy",
+    "145": "awa",
+    "146": "pcm",
+    "147": "bgc",
+    "148": "shn",
+    "149": "oci",
+    "150": "wol",
+    "151": "bci",
+    "152": "kab",
+    "153": "ilo",
+    "154": "bcl",
+    "155": "haw",
+    "156": "mad",
+    "157": "nod",
+    "158": "sag",
+    "159": "sas",
+    "160": "jam",
+    "161": "mey",
+    "162": "shi",
+    "163": "hil",
+    "164": "ace",
+    "165": "kam",
+    "166": "min",
+    "167": "umb",
+    "168": "hno",
+    "169": "ban",
+    "170": "syl",
+    "171": "bxg",
+    "172": "xho",
+    "173": "mww",
+    "174": "epo",
+    "175": "tzm",
+    "176": "zul",
+    "177": "ibo",
+    "178": "abk",
+    "179": "guz",
+    "180": "ckb",
+    "181": "knc",
+    "182": "nso",
+    "183": "bho",
+    "184": "dje",
+    "185": "tiv",
+    "186": "gle",
+    "187": "lua",
+    "188": "skr",
+    "189": "bto",
+    "190": "kea",
+    "191": "glk",
+    "192": "ast",
+    "193": "sat",
+    "194": "ktu",
+    "195": "bhb",
+    "196": "emk",
+    "197": "kng",
+    "198": "kmb",
+    "199": "tsn",
+    "200": "gom",
+    "201": "ven",
+    "202": "sco",
+    "203": "glv",
+    "204": "sot",
+    "205": "sou",
+    "206": "gno",
+    "207": "nde",
+    "208": "bjn",
+    "209": "ina",
+    "210": "fmu",
+    "211": "esg",
+    "212": "wes",
+    "213": "pnb",
+    "214": "phr",
+    "215": "mui",
+    "216": "bug",
+    "217": "mrr",
+    "218": "kas",
+    "219": "lir",
+    "220": "vah",
+    "221": "ssw",
+    "222": "rwr",
+    "223": "pcc",
+    "224": "hms",
+    "225": "wbr",
+    "226": "swv",
+    "227": "mtr",
+    "228": "haz",
+    "229": "aii",
+    "230": "bns",
+    "231": "msi",
+    "232": "wuu",
+    "233": "hsn",
+    "234": "bgp",
+    "235": "tts",
+    "236": "lmn",
+    "237": "dcc",
+    "238": "bew",
+    "239": "bjj",
+    "240": "ibb",
+    "241": "tji",
+    "242": "hoj",
+    "243": "cpx",
+    "244": "cdo",
+    "245": "daq",
+    "246": "mut",
+    "247": "nap",
+    "248": "czh",
+    "249": "gdx",
+    "250": "sdh",
+    "251": "scn",
+    "252": "mnp",
+    "253": "bar",
+    "254": "mzn",
+    "255": "gsw"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "label2id": null,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.05,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 48,
+  "num_negatives": 100,
+  "output_hidden_size": 1280,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 1024,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 154,
+  "xvector_output_dim": 512
+}

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

model.safetensors

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preprocessor_config.json

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+{
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+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
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+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

pytorch_model.bin

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