Update handler.py
Browse files- handler.py +6 -7
handler.py
CHANGED
@@ -1,6 +1,5 @@
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from typing import
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from pyannote.audio import Pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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import torch
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import base64
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import numpy as np
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@@ -22,16 +21,16 @@ class EndpointHandler():
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"""
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# process input
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None) #
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# decode the base64 audio data
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audio_data = base64.b64decode(inputs)
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audio_nparray = np.frombuffer(audio_data, dtype=np.int16)
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# prepare pynannote input
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audio_tensor= torch.from_numpy(audio_nparray).unsqueeze(0)
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pyannote_input = {"waveform": audio_tensor, "sample_rate": SAMPLE_RATE}
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# apply pretrained pipeline
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# pass inputs with all kwargs in data
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if parameters is not None:
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@@ -45,4 +44,4 @@ class EndpointHandler():
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for segment, _, label in diarization.itertracks(yield_label=True)
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]
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return {"diarization": processed_diarization}
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from typing import Dict
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from pyannote.audio import Pipeline
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import torch
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import base64
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import numpy as np
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"""
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# process input
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None) # min_speakers=2, max_speakers=5
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# decode the base64 audio data
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audio_data = base64.b64decode(inputs)
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audio_nparray = np.frombuffer(audio_data, dtype=np.int16)
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# prepare pynannote input
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audio_tensor= torch.from_numpy(audio_nparray).float().unsqueeze(0)
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pyannote_input = {"waveform": audio_tensor, "sample_rate": SAMPLE_RATE}
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# apply pretrained pipeline
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# pass inputs with all kwargs in data
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if parameters is not None:
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for segment, _, label in diarization.itertracks(yield_label=True)
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]
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return {"diarization": processed_diarization}
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