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# Install required packages
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from pydub import AudioSegment
import os
import gradio as gr
# Load the model and processor
model_id = "hackergeek98/tinyyyy_whisper"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
processor = AutoProcessor.from_pretrained(model_id)
# Create pipeline
whisper_pipe = pipeline(
"automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=0 if torch.cuda.is_available() else -1
)
# Convert audio to WAV format
def convert_to_wav(audio_path):
audio = AudioSegment.from_file(audio_path)
wav_path = "converted_audio.wav"
audio.export(wav_path, format="wav")
return wav_path
# Split long audio into chunks
def split_audio(audio_path, chunk_length_ms=30000): # Default: 30 sec per chunk
audio = AudioSegment.from_wav(audio_path)
chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
chunk_paths = []
for i, chunk in enumerate(chunks):
chunk_path = f"chunk_{i}.wav"
chunk.export(chunk_path, format="wav")
chunk_paths.append(chunk_path)
return chunk_paths
# Transcribe a long audio file
def transcribe_long_audio(audio_path):
wav_path = convert_to_wav(audio_path)
chunk_paths = split_audio(wav_path)
transcription = ""
for chunk in chunk_paths:
result = whisper_pipe(chunk)
transcription += result["text"] + "\n"
os.remove(chunk) # Remove processed chunk
os.remove(wav_path) # Cleanup original file
return transcription
# Gradio interface
def transcribe_interface(audio_file):
return transcribe_long_audio(audio_file)
iface = gr.Interface(
fn=transcribe_interface,
inputs=gr.Audio(source="upload", type="filepath"),
outputs="text",
title="Whisper ASR - Transcription",
description="Upload an audio file, and the model will transcribe it."
)
if __name__ == "__main__":
iface.launch()
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