Plachta
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
from moviepy.editor import AudioFileClip
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import whisper
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import os
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import torchaudio
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parent_dir = "../"
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filelist = ["taffy1.mp4", "taffy2.mp4"]
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for file in filelist:
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my_audio_clip = AudioFileClip(parent_dir + file)
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my_audio_clip.write_audiofile(parent_dir + file.rstrip(".mp4") + ".wav")
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for file in filelist:
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file = file.replace(".mp4", ".wav")
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os.system(f"demucs --two-stems=vocals {parent_dir}{file}")
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for file in filelist:
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file = file.strip(".mp4")
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wav, sr = torchaudio.load(f"./separated/htdemucs/{file}/vocals.wav", frame_offset=0, num_frames=-1, normalize=True,
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channels_first=True)
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# merge two channels into one
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wav = wav.mean(dim=0).unsqueeze(0)
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if sr != 22050:
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wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(wav)
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torchaudio.save(file + ".wav", wav, 22050, channels_first=True)
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model = whisper.load_model("medium")
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def transcribe_one(audio_path):
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# load audio and pad/trim it to fit 30 seconds
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audio = whisper.load_audio(audio_path)
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audio = whisper.pad_or_trim(audio)
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# make log-Mel spectrogram and move to the same device as the model
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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# detect the spoken language
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_, probs = model.detect_language(mel)
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lang = max(probs, key=probs.get)
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# decode the audio
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options = whisper.DecodingOptions()
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result = whisper.decode(model, mel, options)
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# print the recognized text
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return result
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result = model.transcribe("taffy2.wav")
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# segment audio based on segment results
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