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import soundfile as sf | |
import torch | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
import gradio as gr | |
import sox | |
import os | |
def convert(inputfile, outfile): | |
sox_tfm = sox.Transformer() | |
sox_tfm.set_output_format( | |
file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 | |
) | |
sox_tfm.build(inputfile, outfile) | |
api_token = os.getenv("API_TOKEN") | |
model_name = "indonesian-nlp/wav2vec2-indonesian-javanese-sundanese" | |
processor = Wav2Vec2Processor.from_pretrained(model_name, use_auth_token=api_token) | |
model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=api_token) | |
def parse_transcription(wav_file): | |
filename = wav_file.name.split('.')[0] | |
convert(wav_file.name, filename + "16k.wav") | |
speech, _ = sf.read(filename + "16k.wav") | |
input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values | |
logits = model(input_values).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) | |
return transcription | |
output = gr.outputs.Textbox(label="The transcript") | |
input_ = gr.inputs.Audio(source="microphone", type="file") | |
gr.Interface(parse_transcription, inputs=input_, outputs=[output], | |
analytics_enabled=False, | |
show_tips=False, | |
theme='huggingface', | |
layout='vertical', | |
title="Multilingual Speech Recognition for Indonesian Languages", | |
description="Speech Recognition Live Demo for Indonesian, Javanese and Sundanese Language", | |
article="This demo was built for the project " | |
"<a href='https://github.com/indonesian-nlp/multilingual-asr' target='_blank'>Multilingual Speech Recognition for Indonesian Languages</a>. " | |
"It uses the <a href='https://huggingface.co/indonesian-nlp/wav2vec2-indonesian-javanese-sundanese' target='_blank'>indonesian-nlp/wav2vec2-indonesian-javanese-sundanese</a> model " | |
"which was fine-tuned on Indonesian Common Voice, Javanese and Sundanese OpenSLR speech datasets.", | |
enable_queue=True).launch( inline=False) |