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import torch
import spaces
import gradio as gr
import soundfile as sf
import numpy as np
import pytube as pt
import librosa
from transformers import AutoProcessor, Wav2Vec2BertForCTC
MODEL_NAME = "mikr/w2v-bert-2.0-czech-colab-cv16"
device = 0 if torch.cuda.is_available() else "cpu"
processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = Wav2Vec2BertForCTC.from_pretrained(MODEL_NAME).to(device)
@spaces.GPU
def text_from_audio(audio_path):
a, s = librosa.load(audio_path, sr=16_000)
input_values = processor(a, sampling_rate=s, return_tensors="pt").input_features
with torch.no_grad():
logits = model(input_values.to(device)).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe speech
transcription = processor.batch_decode(predicted_ids)
text = transcription[0]
return text
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"
)
elif (microphone is None) and (file_upload is None):
return "ERROR: You have to either use the microphone or upload an audio file"
audio_path = microphone if microphone is not None else file_upload
text = text_from_audio(audio_path)
return warn_output + text
def _return_yt_html_embed(yt_url):
video_id = yt_url.split("?v=")[-1]
HTML_str = (
f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
" </center>"
)
return HTML_str
def yt_transcribe(yt_url):
yt = pt.YouTube(yt_url)
html_embed_str = _return_yt_html_embed(yt_url)
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio.mp3")
text = text_from_audio("audio.mp3")
return html_embed_str, text
demo = gr.Blocks()
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="microphone", type="filepath"),
gr.Audio(sources="upload", type="filepath"),
],
outputs="text",
title="W2V Bert 2.0 Demo: Transcribe Czech Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the fine-tuned"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) "
"and 🤗 Transformers to transcribe audio files of arbitrary length."
),
allow_flagging="never",
)
yt_transcribe = gr.Interface(
fn=yt_transcribe,
inputs=[gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
outputs=["html", "text"],
title="W2V Bert 2.0 Demo: Transcribe Czech YouTube Video",
description=(
"Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
" arbitrary length."
),
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
demo.launch(server_name="0.0.0.0")
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