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import gradio as gr
import librosa
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = SpeechT5ForSpeechToText.from_pretrained("openai/whisper-large")
model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
def process_audio(sampling_rate, waveform):
# convert from int16 to floating point
waveform = waveform / 32678.0
# convert to mono if stereo
if len(waveform.shape) > 1:
waveform = librosa.to_mono(waveform.T)
# resample to 16 kHz if necessary
if sampling_rate != 16000:
waveform = librosa.resample(waveform, orig_sr=sampling_rate, target_sr=16000)
# limit to 30 seconds
waveform = waveform[:16000*30]
# make PyTorch tensor
waveform = torch.tensor(waveform)
return waveform
def predict(audio, mic_audio=None):
# audio = tuple (sample_rate, frames) or (sample_rate, (frames, channels))
if mic_audio is not None:
sampling_rate, waveform = mic_audio
elif audio is not None:
sampling_rate, waveform = audio
else:
return "(please provide audio)"
waveform = process_audio(sampling_rate, waveform)
input_features = processor(waveform, sampling_rate=16000, return_tensors="pt").input_features
predicted_ids = model.generate(input_features, max_length=400)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
return transcription[0]
title = "Demo for Whisper -> Something -> XLS-R"
description = """
<b>How to use:</b> Upload an audio file or record using the microphone. The audio is converted to mono and resampled to 16 kHz before
being passed into the model. The output is the text transcription of the audio.
"""
gr.Interface(
fn=predict,
inputs=[
gr.Audio(label="Upload Speech", source="upload", type="numpy"),
gr.Audio(label="Record Speech", source="microphone", type="numpy"),
],
outputs=[
gr.Text(label="Transcription"),
],
title=title,
article=article,
).launch() |