Arabic speech recognition, classification and text-to-speech.

Related tags

Text Data & NLPttsarabicasrarabic-speech-recognition
Overview

klaam

Arabic speech recognition, classification and text-to-speech using many advanced models like wave2vec and fastspeech2. This repository allows training and prediction using pretrained models.

Usage

from klaam import SpeechClassification
model = SpeechClassification()
model.classify(wav_file)

from klaam import SpeechRecognition
model = SpeechRecognition()
model.transcribe(wav_file)

from klaam import TextToSpeech
model = TextToSpeech()
model.synthesize(sample_text)

There are two avilable models for recognition trageting MSA and egyptian dialect . You can set any of them using the lang attribute

 from klaam import SpeechRecognition
 model = SpeechRecognition(lang = 'msa')
 model.transcribe('file.wav')

Datasets

Dataset Description link
MGB-3 Egyptian Arabic Speech recognition in the wild. Every sentence was annotated by four annotators. More than 15 hours have been collected from YouTube. requires registeration here
ADI-5 More than 50 hours collected from Aljazeera TV. 4 regional dialectal: Egyptian (EGY), Levantine (LAV), Gulf (GLF), North African (NOR), and Modern Standard Arabic (MSA). This dataset is a part of the MGB-3 challenge. requires registeration here
Common voice Multlilingual dataset avilable on huggingface here.
Arabic Speech Corpus Arabic dataset with alignment and transcriptions here.

Models

We currently support four models, three of them are avilable on transformers.

Language Description Source
Egyptian Speech recognition wav2vec2-large-xlsr-53-arabic-egyptian
Standard Arabic Speech recognition wav2vec2-large-xlsr-53-arabic
EGY, NOR, LAV, GLF, MSA Speech classification wav2vec2-large-xlsr-dialect-classification
Standard Arabic Text-to-Speech fastspeech2

Example Notebooks

Name Description Notebook
Demo Classification, Recongition and Text-to-speech in a few lines of code.
Demo with mic Audio Recongition and classification with recording.

Training

The scripts are a modification of jqueguiner/wav2vec2-sprint.

classification

This script is used for the classification task on the 5 classes.

python run_classifier.py \
   --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
   --output_dir=/path/to/output \
   --cache_dir=/path/to/cache/ \
   --freeze_feature_extractor \
   --num_train_epochs="50" \
   --per_device_train_batch_size="32" \
   --preprocessing_num_workers="1" \
   --learning_rate="3e-5" \
   --warmup_steps="20" \
   --evaluation_strategy="steps"\
   --save_steps="100" \
   --eval_steps="100" \
   --save_total_limit="1" \
   --logging_steps="100" \
   --do_eval \
   --do_train \

Recognition

This script is for training on the dataset for pretraining on the egyption dialects dataset.

python run_mgb3.py \
    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
    --output_dir=/path/to/output \
    --cache_dir=/path/to/cache/ \
    --freeze_feature_extractor \
    --num_train_epochs="50" \
    --per_device_train_batch_size="32" \
    --preprocessing_num_workers="1" \
    --learning_rate="3e-5" \
    --warmup_steps="20" \
    --evaluation_strategy="steps"\
    --save_steps="100" \
    --eval_steps="100" \
    --save_total_limit="1" \
    --logging_steps="100" \
    --do_eval \
    --do_train \

This script can be used for Arabic common voice training

python run_common_voice.py \
    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
    --dataset_config_name="ar" \
    --output_dir=/path/to/output/ \
    --cache_dir=/path/to/cache \
    --overwrite_output_dir \
    --num_train_epochs="1" \
    --per_device_train_batch_size="32" \
    --per_device_eval_batch_size="32" \
    --evaluation_strategy="steps" \
    --learning_rate="3e-4" \
    --warmup_steps="500" \
    --fp16 \
    --freeze_feature_extractor \
    --save_steps="10" \
    --eval_steps="10" \
    --save_total_limit="1" \
    --logging_steps="10" \
    --group_by_length \
    --feat_proj_dropout="0.0" \
    --layerdrop="0.1" \
    --gradient_checkpointing \
    --do_train --do_eval \
    --max_train_samples 100 --max_val_samples 100

Text To Speech

We use the pytorch implementation of fastspeech2 by ming024. The procedure is as follows

Download the dataset

wget http://en.arabicspeechcorpus.com/arabic-speech-corpus.zip 
unzip arabic-speech-corpus.zip

Create multiple directories for data

mkdir -p raw_data/Arabic/Arabic preprocessed_data/Arabic/TextGrid/Arabic
cp arabic-speech-corpus/textgrid/* preprocessed_data/Arabic/TextGrid/Arabic

Prepare metadata

import os 
base_dir = '/content/arabic-speech-corpus'
lines = []
for lab_file in os.listdir(f'{base_dir}/lab'):
  lines.append(lab_file[:-4]+'|'+open(f'{base_dir}/lab/{lab_file}', 'r').read())


open(f'{base_dir}/metadata.csv', 'w').write(('\n').join(lines))

Clone my fork

git clone --depth 1 https://github.com/zaidalyafeai/FastSpeech2
cd FastSpeech2
pip install -r requirements.txt

Prepare alignments and prepreocessed data

python3 prepare_align.py config/Arabic/preprocess.yaml
python3 preprocess.py config/Arabic/preprocess.yaml

Unzip vocoders

unzip hifigan/generator_LJSpeech.pth.tar.zip -d hifigan
unzip hifigan/generator_universal.pth.tar.zip -d hifigan

Start training

python3 train.py -p config/Arabic/preprocess.yaml -m config/Arabic/model.yaml -t config/Arabic/train.yaml
Owner
ARBML
Arabic NLP
ARBML
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