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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 9 16:43:31 2024
@author: Pradeep Kumar
"""
import whisper
import torch
import os
from flask import Flask, request, abort, jsonify, render_template
from deep_translator import GoogleTranslator
#%%
import subprocess
# List of packages to check versions for
packages = ["whisper", "torch", "os", "flask", "deep-translator"]
# Dictionary to store versions
package_versions = {}
for package in packages:
try:
# Run pip show to get version info
result = subprocess.run(
["pip", "show", package],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True
)
if result.returncode == 0:
# Parse the version from the output
for line in result.stdout.splitlines():
if line.startswith("Version:"):
package_versions[package] = line.split(":", 1)[1].strip()
else:
package_versions[package] = "Not Installed"
except Exception as e:
package_versions[package] = f"Error: {str(e)}"
package_versions
#%%
# Check if NVIDIA GPU is available
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize Flask app
app = Flask(__name__)
# Directories for transcripts
BASE_DIR = os.getcwd()
TRANSCRIPTS_FOLDER = os.path.join(BASE_DIR, 'transcripts')
# Ensure transcripts directory exists
def check_directory(path):
if not os.path.exists(path):
os.makedirs(path)
check_directory(TRANSCRIPTS_FOLDER)
@app.route('/')
def upload_page():
"""
Render the upload page for audio file submission.
"""
return render_template('upload.html')
@app.route('/process_audio', methods=['POST'])
def process_audio():
"""
Process audio directly from the destination using Whisper.
"""
if 'audio_file' not in request.files:
return abort(400, "No file part in the request.")
audio_file = request.files['audio_file']
selected_language = request.form.get('language', None)
model_type = request.form.get('model_type', "base")
if not audio_file or audio_file.filename == '':
return abort(400, "No file selected for upload.")
# Save the uploaded file to a temporary location
temp_audio_path = os.path.join(BASE_DIR, audio_file.filename)
audio_file.save(temp_audio_path)
try:
# Load the Whisper model based on user selection
model = whisper.load_model(model_type, device=DEVICE)
except Exception as e:
return jsonify({"error": f"Failed to load Whisper model ({model_type}): {e}"}), 500
try:
# Transcribe with the user-selected language
if selected_language:
result = model.transcribe(temp_audio_path,fp16=False, language=selected_language, verbose=False)
else:
return abort(400, "Language selection is required.")
# Save the transcription with timestamps
transcript_file = os.path.join(TRANSCRIPTS_FOLDER, f"{audio_file.filename}_transcript.txt")
with open(transcript_file, 'w', encoding='utf-8') as text_file:
for segment in result['segments']:
start_time = segment['start']
end_time = segment['end']
text = segment['text']
text_file.write(f"[{start_time:.2f} - {end_time:.2f}] {text}\n")
if selected_language == 'nl':
text_en = GoogleTranslator(source='auto', target='en').translate(text)
text_file.write(f"[{start_time:.2f} - {end_time:.2f}] {text_en}\n")
# Return the transcription metadata
return jsonify({
"message": "Transcription successful!",
"transcript_path": transcript_file,
"transcription_preview": result['text']
})
except Exception as e:
return jsonify({"error": f"Failed to process the audio file: {e}"}), 500
finally:
# Clean up temporary audio file
if os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
if __name__ == '__main__':
# Run the Flask application
app.run(debug=True)
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