Spaces:
Running
on
Zero
Running
on
Zero
import os | |
import torch | |
import gradio as gr | |
import pytube as pt | |
import spaces | |
from transformers import pipeline | |
from huggingface_hub import model_info | |
MODEL_NAME = os.environ.get("MODEL_NAME", "NbAiLab/salmon-whisper-large-smj-lr7e-5") | |
MODEL_VERSION = os.environ.get("MODEL_VERSION", None) | |
lang = "fi" | |
share = (os.environ.get("SHARE", "False")[0].lower() in "ty1") or None | |
auth_token = os.environ.get("AUTH_TOKEN") or True | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
print(f"Using device: {device}") | |
def pipe(file, return_timestamps=False): | |
asr = pipeline( | |
task="automatic-speech-recognition", | |
model=MODEL_NAME, | |
revision=MODEL_VERSION, | |
chunk_length_s=30, | |
device=device, | |
token=auth_token, | |
) | |
asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids( | |
language=lang, | |
task="transcribe", | |
no_timestamps=not return_timestamps, | |
) | |
# asr.model.config.no_timestamps_token_id = asr.tokenizer.encode("<|notimestamps|>", add_special_tokens=False)[0] | |
return asr(file, return_timestamps=return_timestamps) | |
def transcribe(file, return_timestamps=False): | |
if not return_timestamps: | |
text = pipe(file)["text"] | |
else: | |
chunks = pipe(file, return_timestamps=True)["chunks"] | |
text = [] | |
for chunk in chunks: | |
start_time = time.strftime('%H:%M:%S', time.gmtime(chunk["timestamp"][0])) if chunk["timestamp"][0] is not None else "??:??:??" | |
end_time = time.strftime('%H:%M:%S', time.gmtime(chunk["timestamp"][1])) if chunk["timestamp"][1] is not None else "??:??:??" | |
line = f"[{start_time} -> {end_time}] {chunk['text']}" | |
text.append(line) | |
text = "\n".join(text) | |
return 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, return_timestamps=False): | |
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 = transcribe("audio.mp3", return_timestamps=return_timestamps) | |
return html_embed_str, text | |
demo = gr.Blocks() | |
mf_transcribe = gr.Interface( | |
fn=transcribe, | |
inputs=[ | |
gr.components.Audio(sources=['upload', 'microphone'], type="filepath"), | |
# gr.components.Checkbox(label="Return timestamps"), | |
], | |
outputs="text", | |
theme="huggingface", | |
title="Whisper Demo: Transcribe Audio", | |
description=( | |
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the 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.components.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), | |
# gr.components.Checkbox(label="Return timestamps"), | |
], | |
examples=[["https://www.youtube.com/watch?v=mukeSSa5GKo"]], | |
outputs=["html", "text"], | |
theme="huggingface", | |
title="Whisper Demo: Transcribe YouTube", | |
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(share=True).queue() | |