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import torch | |
from transformers import pipeline | |
import os | |
import gradio as gr | |
from pydub import AudioSegment | |
from pytube import YouTube | |
import timeit | |
import math | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
pipe = pipeline("automatic-speech-recognition", model="distil-whisper/distil-medium.en", device=device) | |
def transcribe_speech_local(filepath): | |
if filepath is None: | |
return [{"error": "No audio found, please retry."}] | |
# Split audio into 15-second chunks | |
audio = AudioSegment.from_file(filepath) | |
chunk_length_ms = 15000 # 15 seconds in milliseconds | |
chunks = [audio[i:i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)] | |
print(chunks) | |
aligned_chunks = [] | |
transcription_time_total = 0 | |
# Transcribe each chunk and measure time | |
for chunk_id, chunk in enumerate(chunks): | |
start_time = timeit.default_timer() | |
chunk.export("temp_chunk.wav", format="wav") | |
output = pipe("temp_chunk.wav") | |
transcription_time = timeit.default_timer() - start_time | |
transcription_time_total += transcription_time | |
# Calculate start and end times in seconds | |
start_time_sec = chunk_id * 15 | |
end_time_sec = start_time_sec + len(chunk) / 1000.0 | |
aligned_chunks.append({ | |
"chunk_id": chunk_id, | |
"chunk_length": len(chunk) / 1000.0, | |
"text": output["text"], | |
"start_time": start_time_sec, | |
"end_time": end_time_sec, | |
"transcription_time": transcription_time | |
}) | |
return aligned_chunks | |
def download_audio_from_youtube(youtube_url): | |
yt = YouTube(youtube_url) | |
stream = yt.streams.filter(only_audio=True).first() | |
output_path = stream.download() | |
base, ext = os.path.splitext(output_path) | |
audio_file = base + '.mp3' | |
os.rename(output_path, audio_file) | |
return audio_file | |
def transcribe_speech_from_youtube(youtube_url): | |
audio_filepath = download_audio_from_youtube(youtube_url) | |
# Convert to WAV format with 16kHz sample rate if necessary | |
audio = AudioSegment.from_file(audio_filepath) | |
audio = audio.set_frame_rate(16000).set_channels(1) | |
audio.export("converted_audio.wav", format="wav") | |
audio = AudioSegment.from_file("converted_audio.wav") | |
# Split audio into 15-second chunks | |
chunk_length_ms = 15000 # 15 seconds in milliseconds | |
chunks = [audio[i:i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)] | |
aligned_chunks = [] | |
transcription_time_total = 0 | |
# Transcribe each chunk and measure time | |
for chunk_id, chunk in enumerate(chunks): | |
start_time = timeit.default_timer() | |
chunk.export("temp_chunk.wav", format="wav") | |
output = pipe("temp_chunk.wav") | |
transcription_time = timeit.default_timer() - start_time | |
transcription_time_total += transcription_time | |
# Calculate start and end times in seconds | |
start_time_sec = chunk_id * 15 | |
end_time_sec = start_time_sec + len(chunk) / 1000.0 | |
aligned_chunks.append({ | |
"chunk_id": chunk_id, | |
"chunk_length": len(chunk) / 1000.0, | |
"text": output["text"], | |
"start_time": start_time_sec, | |
"end_time": end_time_sec, | |
"transcription_time": transcription_time | |
}) | |
# Clean up temporary files | |
if os.path.exists("temp_chunk.wav"): | |
os.remove("temp_chunk.wav") | |
if os.path.exists("converted_audio.wav"): | |
os.remove("converted_audio.wav") | |
if os.path.exists(audio_filepath): | |
os.remove(audio_filepath) | |
return aligned_chunks | |
file_transcribe = gr.Interface( | |
fn=transcribe_speech_local, | |
inputs=gr.Audio(sources="upload", type="filepath"), | |
outputs=gr.JSON(label="Transcription with Time Alignment"), | |
allow_flagging="never" | |
) | |
link_transcribe = gr.Interface( | |
fn=transcribe_speech_from_youtube, | |
inputs=gr.Textbox(lines=1, placeholder="Enter YouTube URL here...", label="YouTube URL"), | |
outputs=gr.JSON(label="Transcription with Time Alignment"), | |
allow_flagging="never" | |
) | |
demo = gr.TabbedInterface( | |
[file_transcribe, link_transcribe ], | |
["Local files(mp3/mp4/wav)", "Links"] | |
) | |
demo.launch(share=True) | |