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from yt_dlp import YoutubeDL
from moviepy.editor import AudioFileClip
from faster_whisper import WhisperModel
import subprocess
from pyngrok import ngrok
from flask import Flask, render_template, request, jsonify
import os
app = Flask(__name__)
def get_output_webm_paths(folder_path):
for root, dirs, files in os.walk(folder_path):
for file in files:
if file.startswith("output.webm"):
file_path = os.path.join(root, file)
return file_path
def transcribe_video(video_path):
youtube_link=video_path
ydl_opts = {
'outtmpl': 'output.webm',
}
with YoutubeDL(ydl_opts) as ydl:
ydl.download([youtube_link])
output_webm_path = get_output_webm_paths("/content/drive/MyDrive/Prometheus_RVC/transcript")
audio = AudioFileClip(output_webm_path)
audio.write_audiofile("/content/drive/MyDrive/Prometheus_RVC/transcript/output.wav")
AUDIO_FILE_NAME = "/content/drive/MyDrive/Prometheus_RVC/transcript/output.wav"
model_size = "medium"
model = WhisperModel(model_size, device="cuda", compute_type="float16")
segments, info = model.transcribe(AUDIO_FILE_NAME, beam_size=5)
output_file = "/content/drive/MyDrive/Prometheus_RVC/transcript/transcription.txt" # 出力ファイル名
with open(output_file, "w") as file:
for segment in segments:
file.write("%s\n" % segment.text)
text = open(output_file, "r").read()
os.remove(output_webm_path)
os.remove("/content/drive/MyDrive/Prometheus_RVC/transcript/output.wav")
return text
@app.route('/transcript', methods=['POST'])
def transcript():
data = request.get_json()
video_url = data['video_url']
transcript = transcribe_video(video_url)
return jsonify({'transcript': transcript})
if __name__ == "__main__":
ngrok_tunnel = ngrok.connect(5000)
http_tunnel = ngrok.connect(addr="5000", proto="http", hostname="measured-smashing-piglet.ngrok-free.app")
print(f"ngrok tunnel URL: {ngrok_tunnel.public_url}")
app.run() |