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Try Audio to Blog

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This commit is contained in:
AjaySi
2024-04-08 17:15:49 +05:30
parent 23b3c7f6e0
commit 54c51e5177
9 changed files with 171 additions and 364 deletions
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73
lib/gpt_providers/audio_to_text_generation/stt_audio_blog.py
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@@ -34,22 +34,27 @@ def speech_to_text(video_url, output_path='.'):
SystemExit: If a critical error occurs that prevents successful execution.
"""
try:
logger.info(f"Accessing YouTube URL: {video_url}")
yt = YouTube(video_url, on_progress_callback=progress_function)
audio_file = None
if video_url.startswith("https://www.youtube.com/") or video_url.startswith("http://www.youtube.com/"):
logger.info(f"Accessing YouTube URL: {video_url}")
yt = YouTube(video_url, on_progress_callback=progress_function)
logger.info("Fetching the highest quality audio stream")
audio_stream = yt.streams.filter(only_audio=True).first()
logger.info("Fetching the highest quality audio stream")
audio_stream = yt.streams.filter(only_audio=True).first()
if audio_stream is None:
logger.warning("No audio stream found for this video.")
return None
if audio_stream is None:
logger.warning("No audio stream found for this video.")
return None
#logger.info(f"Downloading audio for: {yt.title}")
global progress_bar
progress_bar = tqdm(total=1.0, unit='iB', unit_scale=True, desc=yt.title)
audio_file = audio_stream.download(output_path)
progress_bar.close()
logger.info(f"Audio downloaded: {yt.title} to {output_path}")
logger.info(f"Downloading audio for: {yt.title}")
global progress_bar
progress_bar = tqdm(total=1.0, unit='iB', unit_scale=True, desc=yt.title)
audio_file = audio_stream.download(output_path)
progress_bar.close()
logger.info(f"Audio downloaded: {yt.title} to {output_path}")
# Audio filepath from local directory.
elif os.path.exists(audio_input):
audio_file = video_url
# Checking file size
max_file_size = 24 * 1024 * 1024 # 24MB
@@ -59,6 +64,8 @@ def speech_to_text(video_url, output_path='.'):
logger.info(f"Downloaded Audio Size is: {file_size_MB:.2f} MB")
if file_size > max_file_size:
logger.error("File size exceeds 24MB limit.")
# FIXME: We can chunk hour long videos, the code is not tested.
#long_video(audio_file)
sys.exit("File size limit exceeded.")
try:
@@ -86,3 +93,43 @@ def speech_to_text(video_url, output_path='.'):
if os.path.exists(audio_file):
os.remove(audio_file)
logger.info("Temporary audio file removed.")
def long_video(temp_file_name):
"""
Transcribes a YouTube video using OpenAI's Whisper API by processing the video in chunks.
This function handles videos longer than the context limit of the Whisper API by dividing the video into
10-minute segments, transcribing each segment individually, and then combining the results.
Key Changes and Notes:
1. Video Splitting: Splits the audio into 10-minute chunks using the moviepy library.
2. Chunk Transcription: Each audio chunk is transcribed separately and the results are concatenated.
3. Temporary Files for Chunks: Uses temporary files for each audio chunk for transcription.
4. Error Handling: Exception handling is included to capture and return any errors during the process.
5. Logging: Process steps are logged for debugging and monitoring.
6. Cleaning Up: Removes temporary files for both the entire video and individual audio chunks after processing.
Args:
video_url (str): URL of the YouTube video to be transcribed.
"""
# Extract audio and split into chunks
app.logger.info(f"Processing the YT video: {temp_file_name}")
full_audio = mp.AudioFileClip(temp_file_name)
duration = full_audio.duration
chunk_length = 600 # 10 minutes in seconds
chunks = [full_audio.subclip(start, min(start + chunk_length, duration)) for start in range(0, int(duration), chunk_length)]
combined_transcript = ""
for i, chunk in enumerate(chunks):
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as audio_chunk_file:
chunk.write_audiofile(audio_chunk_file.name, codec="mp3")
with open(audio_chunk_file.name, "rb") as audio_file:
# Transcribe each chunk using OpenAI's Whisper API
app.logger.info(f"Transcribing chunk {i+1}/{len(chunks)}")
transcript = openai.Audio.transcribe("whisper-1", audio_file)
combined_transcript += transcript['text'] + "\n\n"
# Remove the chunk audio file
os.remove(audio_chunk_file.name)