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import os
import tempfile
from subprocess import Popen, PIPE
import torch
import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from pydub import AudioSegment
# Constants
MODEL_NAME = "openai/whisper-large-v3-turbo"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
# Whisper pipeline
whisper_pipeline = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
# Convert MP4 to MP3
def convert_mp4_to_mp3(mp4_path, mp3_path):
try:
audio = AudioSegment.from_file(mp4_path, format="mp4")
audio.export(mp3_path, format="mp3")
except Exception as e:
raise RuntimeError(f"Error converting MP4 to MP3: {e}")
# Transcribe audio
def transcribe_audio(audio_path):
try:
# Ensure the audio file is read correctly in bytes format
with open(audio_path, "rb") as audio_file:
audio_data = audio_file.read() # Read the file as bytes
# Process the audio file for transcription
inputs = ffmpeg_read(audio_data, whisper_pipeline.feature_extractor.sampling_rate)
inputs = {"array": inputs, "sampling_rate": whisper_pipeline.feature_extractor.sampling_rate}
# Run the transcription pipeline
result = whisper_pipeline(inputs, batch_size=8, return_timestamps=False)
return result["text"]
except Exception as e:
return f"Error during transcription: {e}"
# Gradio Interface Function
def transcribe_file(file):
# Check file type and convert if necessary
if file.name.endswith(".mp4"):
temp_mp3_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
try:
convert_mp4_to_mp3(file.name, temp_mp3_path)
audio_path = temp_mp3_path
except Exception as e:
return f"Error during MP4 to MP3 conversion: {e}"
else:
audio_path = file.name
# Transcribe audio
transcription = transcribe_audio(audio_path)
# Clean up temporary files
if file.name.endswith(".mp4"):
os.remove(temp_mp3_path)
return transcription
# Gradio interface setup
def launch_gradio():
with gr.Blocks() as demo:
gr.Markdown("# Audio Transcription with Whisper Model")
gr.Interface(
fn=transcribe_file,
inputs=gr.File(label="Upload Audio/Video File (MP4 or MP3)"),
outputs=gr.Textbox(label="Transcribed Text"),
)
demo.launch(share=True)
# Run the Gradio app
if __name__ == "__main__":
launch_gradio()
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