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import torch
from transformers import pipeline
import gradio as gr
MODEL_NAME = "Shamik/whisper-small-bn"
BATCH_SIZE = 8
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
# 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, return_timestamps):
outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe",
"language": "bengali"}, 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
demo = gr.Blocks()
mic_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="microphone", type="filepath"),
gr.Checkbox(value=False, label="Return timestamps"),
],
outputs="text",
title="Whisper Bengali Speech Transcription",
description=(
"Transcribe long-form microphone audio 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.Checkbox(value=False, label="Return timestamps"),
],
outputs="text",
title="Whisper Bengali Speech Transcription",
description=(
"Transcribe long-form 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."
),
examples=[
["./example1.flac", False],
["./example1.flac", True],
],
cache_examples=True,
allow_flagging="never",
)
with demo:
gr.TabbedInterface([file_transcribe, mic_transcribe], ["Transcribe Audio File", "Transcribe Microphone"])
# demo.queue()
demo.launch() |