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import gc
import torch
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import tempfile
import os
from time import monotonic
MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
YT_LENGTH_LIMIT_S = 5400 # limit to 1.5 hour YouTube files
device = 'cuda:1' if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
torch_dtype=torch.float16,
chunk_length_s=30,
device=device,
generate_kwargs={"language": "english"}
)
def download_yt_audio(yt_url, filename, time_limit_s=YT_LENGTH_LIMIT_S):
info_loader = youtube_dl.YoutubeDL()
try:
info = info_loader.extract_info(yt_url, download=False)
except youtube_dl.utils.DownloadError as err:
raise ValueError(f"Error downloading video: {str(err)}")
file_length = info["duration"]
if file_length > time_limit_s:
raise ValueError(f"Video is too long. Maximum allowed length is {time_limit_s // 3600} hour(s).")
ydl_opts = {"outtmpl": filename, "format": "bestaudio/best"} # Only download the best available audio format
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
try:
ydl.download([yt_url])
except youtube_dl.utils.ExtractorError as err:
raise ValueError(f"Error extracting audio: {str(err)}")
def transcribe(yt_url, time_limit_s=YT_LENGTH_LIMIT_S):
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "video.mp4")
t0 = monotonic()
download_yt_audio(yt_url, filepath, time_limit_s)
t1 = monotonic()
print(f"Downloaded video in {t1 - t0:.2f} seconds.")
with open(filepath, "rb") as f:
inputs = f.read()
inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
t0 = monotonic()
text = pipe(inputs, batch_size=BATCH_SIZE)["text"]
t1 = monotonic()
print(f"Transcribed video in {t1 - t0:.2f} seconds.")
torch.cuda.empty_cache()
gc.collect()
return text |