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Enhance audio tools
Browse files- audio_tools.py +25 -7
audio_tools.py
CHANGED
@@ -11,18 +11,36 @@ class TranscribeAudioTool(Tool):
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name = "transcribe_audio"
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description = "Transcribe an audio file"
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inputs = {
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"audio": {"type": "
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}
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output_type = "string"
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def setup(self):
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self.model = InferenceClient(model="openai/whisper-large-v3", provider="hf-inference", token=os.getenv("HUGGINGFACE_API_KEY"))
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def forward(self, audio:
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transcribe_audio_tool = TranscribeAudioTool()
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@@ -31,7 +49,7 @@ def audio_to_base64(file_path: str) -> str:
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"""
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Convert an audio file to base64 format
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Args:
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file_path: Path to the audio file
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Returns:
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The audio file in base64 format
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"""
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name = "transcribe_audio"
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description = "Transcribe an audio file"
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inputs = {
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"audio": {"type": "any", "description": "The audio file in base64 format or as an AudioSegment object"}
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}
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output_type = "string"
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def setup(self):
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self.model = InferenceClient(model="openai/whisper-large-v3", provider="hf-inference", token=os.getenv("HUGGINGFACE_API_KEY"))
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def forward(self, audio: any) -> str:
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try:
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# Handle AudioSegment object
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if isinstance(audio, AudioSegment):
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# Convert AudioSegment to base64
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buffer = BytesIO()
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audio.export(buffer, format="wav")
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audio_data = buffer.getvalue()
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# Handle base64 string
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elif isinstance(audio, str):
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audio_data = base64.b64decode(audio)
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else:
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raise ValueError(f"Unsupported audio type: {type(audio)}. Expected base64 string or AudioSegment object.")
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# Create audio segment from the data
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audio_segment = AudioSegment.from_file(BytesIO(audio_data))
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# Transcribe using the model
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result = self.model.automatic_speech_recognition(audio_segment)
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return result["text"]
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except Exception as e:
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raise RuntimeError(f"Error in transcription: {str(e)}")
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transcribe_audio_tool = TranscribeAudioTool()
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"""
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Convert an audio file to base64 format
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Args:
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file_path: Path to the audio file (should be in mp3 format)
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Returns:
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The audio file in base64 format
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"""
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