Create app.py

app.py

@@ -1,71 +0,0 @@
-import gradio as gr
-import torch
-import librosa
-import soundfile
-import nemo.collections.asr as nemo_asr
-import tempfile
-import os
-import uuid
-
-SAMPLE_RATE = 16000
-
-model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("stt_en_conformer_transducer_large")
-model.change_decoding_strategy(None)
-model.eval()
-
-
-def process_audio_file(file):
-    data, sr = librosa.load(file)
-
-    if sr != SAMPLE_RATE:
-        data = librosa.resample(data, sr, SAMPLE_RATE)
-
-    # monochannel
-    data = librosa.to_mono(data)
-    return data
-
-
-def transcribe(Microphone, File_Upload):
-    warn_output = ""
-    if (Microphone is not None) and (File_Upload is not None):
-        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
-                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        file = Microphone
-
-    elif (Microphone is None) and (File_Upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    elif Microphone is not None:
-        file = Microphone
-    else:
-        file = File_Upload
-
-    audio_data = process_audio_file(file)
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
-        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
-
-        transcriptions = model.transcribe([audio_path])
-
-        # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
-        if type(transcriptions) == tuple and len(transcriptions) == 2:
-            transcriptions = transcriptions[0]
-
-    return warn_output + transcriptions[0]
-
-
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
-        gr.inputs.Audio(source="upload", type='filepath', optional=True),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="NeMo Conformer Transducer Large - English",
-    description="Demo for English speech recognition using Conformer Transducers",
-    allow_flagging='never',
-)
-iface.launch(enable_queue=True)