File size: 1,560 Bytes
b51d214 42e246d b51d214 42e246d b51d214 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import whisper
import yt_dlp
import gradio as gr
def yt_download(link):
if not link.strip():
gr.Info("You need to provide a download link.")
print("You need to provide a download link")
return None
ydl_opts = {
'format': 'bestaudio',
'outtmpl': '%(title)s',
'nocheckcertificate': True,
'ignoreerrors': True,
'no_warnings': True,
'quiet': True,
'extractaudio': True,
'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav'}],
'postprocessor_args': [
'-acodec', 'pcm_f32le'
],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
result = ydl.extract_info(link, download=True)
download_path = ydl.prepare_filename(result, outtmpl='%(title)s.wav')
return download_path
def whisper_(input_audio):
model = whisper.load_model("medium")
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(input_audio)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
text_result = result.text
return text_result
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