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import gradio as gr |
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import torch |
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import librosa |
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import soundfile |
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import nemo.collections.asr as nemo_asr |
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import tempfile |
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import os |
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import uuid |
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SAMPLE_RATE = 16000 |
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model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("stt_en_conformer_transducer_large") |
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model.change_decoding_strategy(None) |
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model.eval() |
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def process_audio_file(file): |
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data, sr = librosa.load(file) |
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if sr != SAMPLE_RATE: |
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data = librosa.resample(data, sr, SAMPLE_RATE) |
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data = librosa.to_mono(data) |
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return data |
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def transcribe(file_mic, file_upload): |
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warn_output = "" |
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if (file_mic 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 = file_mic |
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elif (file_mic 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 file_mic is not None: |
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file = file_mic |
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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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with tempfile.TemporaryDirectory() as tmpdir: |
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audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav') |
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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 type(transcriptions) == tuple and len(transcriptions) == 2: |
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transcriptions = transcriptions[0] |
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return warn_output + transcriptions[0] |
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iface = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.inputs.Audio(source="microphone", type='filepath', optional=True), |
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gr.inputs.Audio(source="upload", type='filepath', optional=True), |
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], |
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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 Conformers", |
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enable_queue=True, |
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allow_flagging=False, |
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) |
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iface.launch() |
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