Add nemo inference code
Browse files
app.py
CHANGED
@@ -25,20 +25,20 @@ def process_audio_file(file):
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return data
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-
def transcribe(
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warn_output = ""
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if (
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warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
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"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
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file =
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elif (
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return "ERROR: You have to either use the microphone or upload an audio file"
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elif
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file =
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else:
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file =
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audio_data = process_audio_file(file)
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@@ -47,6 +47,7 @@ def transcribe(file_mic, file_upload):
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soundfile.write(audio_path, audio_data, SAMPLE_RATE)
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transcriptions = model.transcribe([audio_path])
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# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
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if type(transcriptions) == tuple and len(transcriptions) == 2:
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transcriptions = transcriptions[0]
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@@ -63,8 +64,8 @@ iface = gr.Interface(
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outputs="text",
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layout="horizontal",
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theme="huggingface",
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title="NeMo Conformer Transducer Large",
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description="Demo for speech recognition using
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enable_queue=True,
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allow_flagging=False,
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)
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return data
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+
def transcribe(Microphone, File_Upload):
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warn_output = ""
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if (Microphone is not None) and (File_Upload is not None):
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warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
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"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
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+
file = Microphone
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elif (Microphone is None) and (File_Upload is None):
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return "ERROR: You have to either use the microphone or upload an audio file"
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elif Microphone is not None:
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file = Microphone
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else:
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file = File_Upload
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audio_data = process_audio_file(file)
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soundfile.write(audio_path, audio_data, SAMPLE_RATE)
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transcriptions = model.transcribe([audio_path])
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+
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# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
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if type(transcriptions) == tuple and len(transcriptions) == 2:
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transcriptions = transcriptions[0]
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outputs="text",
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layout="horizontal",
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theme="huggingface",
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title="NeMo Conformer Transducer Large - English",
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description="Demo for English speech recognition using Conformer Transducers",
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enable_queue=True,
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allow_flagging=False,
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)
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