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nithinraok
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5773ebb
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Parent(s):
d68b1ee
Update app.py
Browse files
app.py
CHANGED
@@ -15,7 +15,6 @@ speaker_model = EncDecSpeakerLabelModel.from_pretrained("nvidia/speakerverificat
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model.eval()
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def run_diarization(path1):
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print(path1)
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annotation = model(path1, num_workers=0, batch_size=16)
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rttm=annotation.to_rttm()
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df = pd.DataFrame(columns=['start_time', 'end_time', 'speaker', 'text'])
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@@ -65,7 +64,7 @@ def get_transcripts(df, audio_path):
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filename = create_manifest(df,audio_path)
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model = EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/stt_en_fastconformer_transducer_large").to(device)
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model.eval()
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config = OmegaConf.create({"manifest_filepath": filename, 'batch_size':
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dataloader = model._setup_transcribe_dataloader(config)
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hypotheses = []
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@@ -106,8 +105,9 @@ examples = [
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microphone_interface = gr.Interface(
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fn=run_diarization,
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inputs=[gr.Audio(source="microphone", type="filepath",
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outputs=[gr.components.Dataframe(
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title="Offline Speaker Diarization with NeMo",
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description="This demonstration will perform offline speaker diarization on an audio file using nemo",
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article=article,
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@@ -116,12 +116,13 @@ microphone_interface = gr.Interface(
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allow_flagging=False,
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live=False,
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examples=examples,
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)
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upload_interface = gr.Interface(
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fn=run_diarization,
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inputs=[gr.Audio(source="upload", type='filepath',
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outputs=[gr.components.Dataframe(
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title="Offline Speaker Diarization with NeMo",
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description="This demonstration will perform offline speaker diarization on an audio file using nemo",
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article=article,
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@@ -130,8 +131,8 @@ upload_interface = gr.Interface(
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allow_flagging=False,
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live=False,
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examples=examples,
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)
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demo = gr.TabbedInterface([microphone_interface, upload_interface], ["Microphone", "Upload File"])
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demo.launch(enable_queue=True)
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model.eval()
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def run_diarization(path1):
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annotation = model(path1, num_workers=0, batch_size=16)
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rttm=annotation.to_rttm()
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df = pd.DataFrame(columns=['start_time', 'end_time', 'speaker', 'text'])
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filename = create_manifest(df,audio_path)
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model = EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/stt_en_fastconformer_transducer_large").to(device)
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model.eval()
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config = OmegaConf.create({"manifest_filepath": filename, 'batch_size': 2})
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dataloader = model._setup_transcribe_dataloader(config)
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hypotheses = []
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microphone_interface = gr.Interface(
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fn=run_diarization,
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inputs=[gr.Audio(source="microphone", type="filepath", label="Mic Audio")],
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outputs=[gr.components.Dataframe(wrap=True, label='Speaker Diariazation with Speech Recognition',
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row_count=(1, "dynamic"), headers=['start_time', 'end_time', 'speaker', 'text'])],
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title="Offline Speaker Diarization with NeMo",
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description="This demonstration will perform offline speaker diarization on an audio file using nemo",
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article=article,
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allow_flagging=False,
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live=False,
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examples=examples,
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)
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upload_interface = gr.Interface(
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fn=run_diarization,
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inputs=[gr.Audio(source="upload", type='filepath', label='Upload File')],
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outputs=[gr.components.Dataframe(wrap=True, label='Speaker Diariazation with Speech Recognition',
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row_count=(1, "dynamic"), headers=['start_time', 'end_time', 'speaker', 'text'])],
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title="Offline Speaker Diarization with NeMo",
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description="This demonstration will perform offline speaker diarization on an audio file using nemo",
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article=article,
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allow_flagging=False,
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live=False,
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examples=examples,
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)
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demo = gr.TabbedInterface([microphone_interface, upload_interface], ["Microphone", "Upload File"])
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demo.launch(enable_queue=True)
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