app.py

import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import gradio as gr
import sox
import subprocess


def read_file_and_process(wav_file):
    filename = wav_file.split('.')[0]
    filename_16k = filename + "16k.wav"
    resampler(wav_file, filename_16k)
    speech, _ = sf.read(filename_16k)
    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
    
    return inputs


def resampler(input_file_path, output_file_path):
    command = (
        f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
        f"{output_file_path}"
    )
    subprocess.call(command, shell=True)



def parse_transcription(logits):
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
    return transcription

def parse(wav_file):
    input_values = read_file_and_process(wav_file)
    with torch.no_grad():
        logits = model(**input_values).logits
    return parse_transcription(logits)    
   

    
model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
processor = Wav2Vec2Processor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

    
input_ = gr.Audio(source="microphone", type="filepath") 
txtbox = gr.Textbox(
            label="Hindi text output:",
            lines=5
        )

title = "Speech-to-Text (Hindi) using Vakyansh"
description = "Upload a hindi audio clip, and let AI do the hard work of transcribing."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.06678'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>"

gr.Interface(parse, inputs = input_,  outputs=txtbox, title=title, description=description, article = article,
             streaming=True, interactive=True,
             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);