README.md

metadata

language:
  - multilingual
  - ar
  - as
  - br
  - ca
  - cnh
  - cs
  - cv
  - cy
  - de
  - dv
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - hi
  - hsb
  - hu
  - ia
  - id
  - ja
  - ka
  - ky
  - lg
  - lt
  - ly
  - mn
  - mt
  - nl
  - or
  - pl
  - pt
  - ro
  - ru
  - sah
  - sl
  - ta
  - th
  - tr
  - tt
  - uk
  - vi
language_bcp47:
  - fy-NL
  - ga-IE
  - pa-IN
  - rm-sursilv
  - rm-vallader
  - sy-SE
  - zh-CN
  - zh-HK
  - zh-TW
datasets:
  - common_voice
tags:
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
  - speech
  - xlsr-fine-tuning-week
license: apache-2.0
model-index:
  - name: XLSR Wav2Vec2 for 56 language by Voidful
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice
          type: common_voice
        metrics:
          - name: Test CER
            type: cer
            value: 23.21

wav2vec2-xlsr-multilingual-56

56 language, 1 model Multilingual ASR

Fine-tuned facebook/wav2vec2-large-xlsr-53 on 56 language using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.

For more detail: https://github.com/voidful/wav2vec2-xlsr-multilingual-56

Env setup:

!pip install torchaudio
!pip install datasets transformers
!pip install asrp
!wget -O lang_ids.pk https://huggingface.co/voidful/wav2vec2-xlsr-multilingual-56/raw/main/lang_ids.pk

Usage

import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
    AutoTokenizer, 
    AutoModelWithLMHead 
)
import torch
import re
import sys
import soundfile as sf
model_name = "voidful/wav2vec2-xlsr-multilingual-56"
device = "cuda"
processor_name = "voidful/wav2vec2-xlsr-multilingual-56"

import pickle
with open("lang_ids.pk", 'rb') as output:
    lang_ids = pickle.load(output)
    
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2Processor.from_pretrained(processor_name)

model.eval()

def load_file_to_data(file,sampling_rate=16_000):
    batch = {}
    speech, _ = torchaudio.load(file)
    if sampling_rate != '16_000' or sampling_rate != '16000':
        resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16_000)
        batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
        batch["sampling_rate"] = resampler.new_freq
    else:
        batch["speech"] = speech.squeeze(0).numpy()
        batch["sampling_rate"] = '16000'
    return batch


def predict(data):
    features = processor(data["speech"], sampling_rate=data["sampling_rate"], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
        decoded_results = []
        for logit in logits:
            pred_ids = torch.argmax(logit, dim=-1)
            mask = pred_ids.ge(1).unsqueeze(-1).expand(logit.size())
            vocab_size = logit.size()[-1]
            voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1)
            comb_pred_ids = torch.argmax(voice_prob, dim=-1)
            decoded_results.append(processor.decode(comb_pred_ids))

    return decoded_results

def predict_lang_specific(data,lang_code):
    features = processor(data["speech"], sampling_rate=data["sampling_rate"], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
        decoded_results = []
        for logit in logits:
            pred_ids = torch.argmax(logit, dim=-1)
            mask = ~pred_ids.eq(processor.tokenizer.pad_token_id).unsqueeze(-1).expand(logit.size())
            vocab_size = logit.size()[-1]
            voice_prob = torch.nn.functional.softmax((torch.masked_select(logit, mask).view(-1,vocab_size)),dim=-1)
            filtered_input = pred_ids[pred_ids!=processor.tokenizer.pad_token_id].view(1,-1).to(device)
            if len(filtered_input[0]) == 0:
                decoded_results.append("")
            else:
                lang_mask = torch.empty(voice_prob.shape[-1]).fill_(0)
                lang_index = torch.tensor(sorted(lang_ids[lang_code]))
                lang_mask.index_fill_(0, lang_index, 1)
                lang_mask = lang_mask.to(device)
                comb_pred_ids = torch.argmax(lang_mask*voice_prob, dim=-1)
                decoded_results.append(processor.decode(comb_pred_ids))
                
    return decoded_results


predict(load_file_to_data('audio file path',sampling_rate=16_000)) # beware of the audio file sampling rate

predict_lang_specific(load_file_to_data('audio file path',sampling_rate=16_000),'en') # beware of the audio file sampling rate

Result

Common Voice Languages	Num. of data	Hour	WER	CER
ar	21744	81.5	75.29	31.23
as	394	1.1	95.37	46.05
br	4777	7.4	93.79	41.16
ca	301308	692.8	24.80	10.39
cnh	1563	2.4	68.11	23.10
cs	9773	39.5	67.86	12.57
cv	1749	5.9	95.43	34.03
cy	11615	106.7	67.03	23.97
de	262113	822.8	27.03	6.50
dv	4757	18.6	92.16	30.15
el	3717	11.1	94.48	58.67
en	580501	1763.6	34.87	14.84
eo	28574	162.3	37.77	6.23
es	176902	337.7	19.63	5.41
et	5473	35.9	86.87	20.79
eu	12677	90.2	44.80	7.32
fa	12806	290.6	53.81	15.09
fi	875	2.6	93.78	27.57
fr	314745	664.1	33.16	13.94
fy-NL	6717	27.2	72.54	26.58
ga-IE	1038	3.5	92.57	51.02
hi	292	2.0	90.95	57.43
hsb	980	2.3	89.44	27.19
hu	4782	9.3	97.15	36.75
ia	5078	10.4	52.00	11.35
id	3965	9.9	82.50	22.82
it	70943	178.0	39.09	8.72
ja	1308	8.2	99.21	62.06
ka	1585	4.0	90.53	18.57
ky	3466	12.2	76.53	19.80
lg	1634	17.1	98.95	43.84
lt	1175	3.9	92.61	26.81
lv	4554	6.3	90.34	30.81
mn	4020	11.6	82.68	30.14
mt	3552	7.8	84.18	22.96
nl	14398	71.8	57.18	19.01
or	517	0.9	90.93	27.34
pa-IN	255	0.8	87.95	42.03
pl	12621	112.0	56.14	12.06
pt	11106	61.3	53.24	16.32
rm-sursilv	2589	5.9	78.17	23.31
rm-vallader	931	2.3	73.67	21.76
ro	4257	8.7	83.84	21.95
ru	23444	119.1	61.83	15.18
sah	1847	4.4	94.38	38.46
sl	2594	6.7	84.21	20.54
sv-SE	4350	20.8	83.68	30.79
ta	3788	18.4	84.19	21.60
th	4839	11.7	141.87	37.16
tr	3478	22.3	66.77	15.55
tt	13338	26.7	86.80	33.57
uk	7271	39.4	70.23	14.34
vi	421	1.7	96.06	66.25
zh-CN	27284	58.7	89.67	23.96
zh-HK	12678	92.1	81.77	18.82
zh-TW	6402	56.6	85.08	29.07