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Runtime error
Add support for whisper openai API.
Browse files
app.py
CHANGED
@@ -13,11 +13,9 @@ import tempfile
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load_dotenv()
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hg_token = os.getenv("HG_ACCESS_TOKEN")
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if hg_token
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hg_token)
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whisper_ml = whisper.load_model("base")
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else:
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print('''No hugging face access token set.
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You need to set it via an .env or environment variable HG_ACCESS_TOKEN''')
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exit(1)
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@@ -27,6 +25,7 @@ def diarization(audio) -> np.array:
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"""
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Receives a pydub AudioSegment and returns an numpy array with all segments.
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"""
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audio.export("/tmp/dz.wav", format="wav")
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diarization = pipeline("/tmp/dz.wav")
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return pd.DataFrame(list(diarization.itertracks(yield_label=True)),columns=["Segment","Trackname", "Speaker"])
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@@ -50,8 +49,24 @@ def prep_audio(audio_segment):
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def transcribe_row(row, audio):
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segment = audio[row.start_ms:row.end_ms]
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def combine_transcription(segments):
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load_dotenv()
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hg_token = os.getenv("HG_ACCESS_TOKEN")
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open_api_key = os.getenv("OPENAI_API_KEY")
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if hg_token == None:
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print('''No hugging face access token set.
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You need to set it via an .env or environment variable HG_ACCESS_TOKEN''')
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exit(1)
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"""
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Receives a pydub AudioSegment and returns an numpy array with all segments.
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"""
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hg_token)
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audio.export("/tmp/dz.wav", format="wav")
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diarization = pipeline("/tmp/dz.wav")
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return pd.DataFrame(list(diarization.itertracks(yield_label=True)),columns=["Segment","Trackname", "Speaker"])
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def transcribe_row(row, audio):
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segment = audio[row.start_ms:row.end_ms]
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if open_api_key == None:
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whisper_ml = whisper.load_model("base")
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data = prep_audio(segment)
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return whisper_ml.transcribe(data)['text']
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else:
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print("Using openai API")
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# the open ai whisper AI only accepts audio files with a length of at
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# least 0.1 seconds.
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if row['end_ms'] - row['start_ms'] < 100:
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return ""
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import openai
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import tempfile
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temp_file = f"/tmp/{row['Trackname']}.mp3"
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segment.export(temp_file, format="mp3")
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print(temp_file)
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audio_file = open(temp_file, "rb")
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return openai.Audio.translate("whisper-1", audio_file)['text']
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def combine_transcription(segments):
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