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bp400-xlsr

Automatic Speech Recognition
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PyTorch
Portuguese
wav2vec2
audio
speech
portuguese-speech-corpus
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metadata
language: pt
datasets:
  - common_voice
  - mls
  - cetuc
  - lapsbm
  - voxforge
  - tedx
  - sid
metrics:
  - wer
tags:
  - audio
  - speech
  - wav2vec2
  - pt
  - portuguese-speech-corpus
  - automatic-speech-recognition
  - speech
  - PyTorch
  - hf-asr-leaderboard
model-index:
  - name: bp400-xlsr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7.0
          type: mozilla-foundation/common_voice_7_0
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 14
license: apache-2.0

bp400-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset

Paper: https://arxiv.org/abs/2107.11414

This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:

  • CETUC: contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the CETEN-Folha corpus.
  • Common Voice 7.0: is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the oficial site.
  • Lapsbm: "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
  • Multilingual Librispeech (MLS): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like LibriVox. The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese used in this work (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
  • Multilingual TEDx: a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech.
  • Sidney (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation;
  • VoxForge: is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.

These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets.

Dataset Train Valid Test
CETUC 93.9h -- 5.4h
Common Voice 37.6h 8.9h 9.5h
LaPS BM 0.8h -- 0.1h
MLS 161.0h -- 3.7h
Multilingual TEDx (Portuguese) 144.2h -- 1.8h
SID 5.0h -- 1.0h
VoxForge 2.8h -- 0.1h
Total 437.2h 8.9h 21.6h

The original model was fine-tuned using fairseq. This notebook uses a converted version of the original one. The link to the original fairseq model is available here.

Summary

CETUC CV LaPS MLS SID TEDx VF AVG
bp_400 (demonstration below) 0.052 0.140 0.074 0.117 0.121 0.245 0.118 0.124
bp_400 + 3-gram 0.033 0.095 0.046 0.123 0.112 0.212 0.123 0.106
bp_400 + 4-gram (demonstration below) 0.030 0.096 0.043 0.106 0.118 0.229 0.117 0.105
bp_400 + 5-gram 0.033 0.094 0.043 0.123 0.111 0.210 0.123 0.105
bp_400 + Transf. 0.032 0.092 0.036 0.130 0.115 0.215 0.125 0.106

Transcription examples

Text Transcription
alguém sabe a que horas começa o jantar alguém sabe a que horas começo jantar
lila covas ainda não sabe o que vai fazer no fundo lilacovas ainda não sabe o que vai fazer no fundo
que tal um pouco desse bom spaghetti quetá um pouco deste bom ispaguete
hong kong em cantonês significa porto perfumado rongkong en cantones significa porto perfumado
vamos hackear esse problema vamos rackar esse problema
apenas a poucos metros há uma estação de ônibus apenas ha poucos metros á uma estação de ônibus
relâmpago e trovão sempre andam juntos relampagotrevão sempre andam juntos

Demonstration 

MODEL_NAME = "lgris/bp400-xlsr"

Imports and dependencies 

%%capture
!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install datasets
!pip install jiwer
!pip install transformers
!pip install soundfile
!pip install pyctcdecode
!pip install https://github.com/kpu/kenlm/archive/master.zip

import jiwer
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
    Wav2Vec2ForCTC,
    Wav2Vec2Processor,
)
from pyctcdecode import build_ctcdecoder
import torch
import re
import sys

Helpers 

chars_to_ignore_regex = '[\,\?\.\!\;\:\"]'  # noqa: W605

def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = speech.squeeze(0).numpy() 
    batch["sampling_rate"] = 16_000 
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
    batch["target"] = batch["sentence"]
    return batch

def calc_metrics(truths, hypos):
    wers = []
    mers = []
    wils = []
    for t, h in zip(truths, hypos):
        try:
            wers.append(jiwer.wer(t, h))
            mers.append(jiwer.mer(t, h))
            wils.append(jiwer.wil(t, h))
        except: # Empty string?
            pass
    wer = sum(wers)/len(wers)
    mer = sum(mers)/len(mers)
    wil = sum(wils)/len(wils)
    return wer, mer, wil

def load_data(dataset):
    data_files = {'test': f'{dataset}/test.csv'}
    dataset = load_dataset('csv', data_files=data_files)["test"]
    return dataset.map(map_to_array)

Model 

class STT:

    def __init__(self, 
                 model_name, 
                 device='cuda' if torch.cuda.is_available() else 'cpu', 
                 lm=None):
        self.model_name = model_name
        self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
        self.processor = Wav2Vec2Processor.from_pretrained(model_name)
        self.vocab_dict = self.processor.tokenizer.get_vocab()
        self.sorted_dict = {
            k.lower(): v for k, v in sorted(self.vocab_dict.items(), 
                                            key=lambda item: item[1])
        }
        self.device = device
        self.lm = lm
        if self.lm:            
            self.lm_decoder = build_ctcdecoder(
                list(self.sorted_dict.keys()),
                self.lm
            )

    def batch_predict(self, batch):
        features = self.processor(batch["speech"], 
                                  sampling_rate=batch["sampling_rate"][0], 
                                  padding=True, 
                                  return_tensors="pt")
        input_values = features.input_values.to(self.device)
        attention_mask = features.attention_mask.to(self.device)
        with torch.no_grad():
            logits = self.model(input_values, attention_mask=attention_mask).logits
        if self.lm:
            logits = logits.cpu().numpy()
            batch["predicted"] = []
            for sample_logits in logits:
                batch["predicted"].append(self.lm_decoder.decode(sample_logits))
        else:
            pred_ids = torch.argmax(logits, dim=-1)
            batch["predicted"] = self.processor.batch_decode(pred_ids)
        return batch

Download datasets 

%%capture
!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI
!mkdir bp_dataset
!unzip bp_dataset -d bp_dataset/

Tests 

stt = STT(MODEL_NAME)

CETUC 

ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)

CETUC WER: 0.05159104708285062

Common Voice 

ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)

CV WER: 0.14031426198658084

LaPS 

ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)

Laps WER: 0.07432133838383838

MLS 

ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)

MLS WER: 0.11678793514817509

SID 

ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)

Sid WER: 0.12152357273433984

TEDx 

ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)

TEDx WER: 0.24666815906766504

VoxForge 

ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)

VoxForge WER: 0.11873106060606062

Tests with LM 

!rm -rf ~/.cache
!gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP  # trained with wikipedia
stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa')
# !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg  # trained with bp
# stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa')

Cetuc 

ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)

CETUC WER: 0.030266462438593742

Common Voice 

ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)

CV WER: 0.09577710237417715

LaPS 

ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)

Laps WER: 0.043617424242424235

MLS 

ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)

MLS WER: 0.10642133314350002

SID 

ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)

Sid WER: 0.11839021001747055

TEDx 

ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)

TEDx WER: 0.22929952467810416

VoxForge 

ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8) 
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)

VoxForge WER: 0.11716314935064935