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import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import gradio as gr
import sox
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)
model_translate = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
tokenizer_translate = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
inlang='hi'
outlang='en'
tokenizer_translate.src_lang = inlang
def translate(text):
encoded_hi = tokenizer_translate(text, return_tensors="pt")
generated_tokens = model_translate.generate(**encoded_hi, forced_bos_token_id=tokenizer_translate.get_lang_id(outlang))
return tokenizer_translate.batch_decode(generated_tokens, skip_special_tokens=True)[0]
processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
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, translate(transcription)
output1 = gr.outputs.Textbox(label="Hindi Output from ASR")
output2 = gr.outputs.Textbox(label="English Translated Output")
input_ = gr.inputs.Audio(source="microphone", type="file")
#gr.Interface(parse_transcription, inputs = input_, outputs="text",
# analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);
gr.Interface(parse_transcription, inputs = input_, outputs=[output1, output2], analytics_enabled=False,
show_tips=False,
theme='huggingface',
layout='vertical',
title="Vakyansh: Speech To text for Indic Languages",
description="This is a live demo for Speech to Text Translation. Models used: vakyansh wav2vec2 hindi + m2m100", enable_queue=True).launch( inline=False) |