File size: 5,217 Bytes
bce3574 946f3ec bce3574 ae1d5fa c99e855 bce3574 c99e855 bce3574 2157ec8 bce3574 c99e855 bce3574 2157ec8 bce3574 c99e855 bce3574 c99e855 5d11b07 2157ec8 c99e855 bce3574 c99e855 bce3574 c99e855 c0d5650 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
import torch
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
import pytube as pt
MODEL_NAME = "VinayHajare/whisper-small-finetuned-common-voice-mr"
BATCH_SIZE = 8
LANG = "mr"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=LANG)
# Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
if seconds is not None:
milliseconds = round(seconds * 1000.0)
hours = milliseconds // 3_600_000
milliseconds -= hours * 3_600_000
minutes = milliseconds // 60_000
milliseconds -= minutes * 60_000
seconds = milliseconds // 1_000
milliseconds -= seconds * 1_000
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
else:
# we have a malformed timestamp so just return it as is
return seconds
def transcribe(file, task, return_timestamps):
outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
text = outputs["text"]
if return_timestamps:
timestamps = outputs["chunks"]
timestamps = [
f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
for chunk in timestamps
]
text = "\n".join(str(feature) for feature in timestamps)
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, task, return_timestamps):
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")
outputs = pipe("audio.mp3",batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
text = outputs["text"]
if return_timestamps:
timestamps = outputs["chunks"]
timestamps = [
f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
for chunk in timestamps
]
text = "\n".join(str(feature) for feature in timestamps)
return html_embed_str, text
demo = gr.Blocks()
mic_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="microphone", type="filepath"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Checkbox(value=False, label="Return timestamps"),
],
outputs="text",
theme="huggingface",
title="Whisper Demo: Transcribe Marathi Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
allow_flagging="never",
)
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="upload", label="Audio file", type="filepath"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Checkbox(value=False, label="Return timestamps"),
],
outputs="text",
theme="huggingface",
title="Whisper Demo: Transcribe Marathi Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
cache_examples=True,
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 Video URL"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Checkbox(value=False, label="Return timestamps"),
],
outputs=["html", "text"],
theme="huggingface",
title="Whisper Demo: Transcribe Marathi 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([mic_transcribe, file_transcribe, yt_transcribe], ["Transcribe Microphone", "Transcribe Audio File", "Transcribe YouTube Video"])
demo.queue(max_size=10)
demo.launch() |