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import gradio as gr | |
import librosa | |
import torch | |
from transformers import SpeechT5Processor, SpeechT5ForSpeechToText | |
checkpoint = "microsoft/speecht5_asr" | |
processor = SpeechT5Processor.from_pretrained(checkpoint) | |
model = SpeechT5ForSpeechToText.from_pretrained(checkpoint) | |
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) | |
inputs = processor(audio=waveform, sampling_rate=16000, return_tensors="pt") | |
predicted_ids = model.generate(**inputs, max_length=400) | |
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
return transcription[0] | |
title = " 😍🥰Prolove 🧑🎤 👨🎤 " | |
description = """aplikasi prolove merupakan aplikasi untuk membantu ejaan kata yang diucapkan oleh user dalam bahasa inggris menjadi benar""" | |
article = """ | |
<div style='margin:20px auto;'> | |
@article{Ao2021SpeechRecog, | |
title = {PROLOVE}, | |
author = {M_ALVI_ADNAN}, | |
archivePrefix={arXiv}, | |
primaryClass={eess.AS}, | |
year={2021} | |
} | |
</pre> | |
<p>Example sound credits:<p> | |
<ul> | |
<li>"i wanna tell u smth <a href="https://freesound.org/people/InspectorJ/sounds/519189/">InspectorJ</a> (CC BY 4.0 license) | |
<li>"let me know <a href="https://freesound.org/people/acclivity/sounds/24096/">acclivity</a> (CC BY-NC 4.0 license) | |
<li>"lets do it <a href="https://freesound.org/people/JoyOhJoy/sounds/165348/">JoyOhJoy</a> (CC0 license) | |
<li>"listen to me <a href="https://freesound.org/people/Sample_Me/sounds/610529/">Sample_Me</a> (CC0 license) | |
</ul> | |
</div> | |
""" | |
examples = [ | |
["examples/I wanna tell you something_alvi.wav", None], | |
["examples/Let me know_fazrin.wav", None], | |
["examples/Lets do it_arka.wav", None], | |
["examples/Listen to me_shifa.wav", None], | |
] | |
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, | |
description=description, | |
article=article, | |
examples=examples, | |
).launch() |