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Running
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Zero
Running
on
Zero
File size: 3,584 Bytes
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import os
from math import floor
from typing import Optional
import spaces
import torch
import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
# configuration
MODEL_NAME = "kotoba-tech/kotoba-whisper-bilingual-v1.0"
BATCH_SIZE = 16
CHUNK_LENGTH_S = 15
# device setting
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
model_kwargs = {'attn_implementation': 'sdpa'}
else:
torch_dtype = torch.float32
device = "cpu"
model_kwargs = {}
# define the pipeline
pipe = pipeline(
model=MODEL_NAME,
chunk_length_s=CHUNK_LENGTH_S,
batch_size=BATCH_SIZE,
torch_dtype=torch_dtype,
device=device,
model_kwargs=model_kwargs,
trust_remote_code=True
)
def format_time(start: Optional[float], end: Optional[float]):
def _format_time(seconds: Optional[float]):
if seconds is None:
return "complete "
minutes = floor(seconds / 60)
hours = floor(seconds / 3600)
seconds = seconds - hours * 3600 - minutes * 60
m_seconds = floor(round(seconds - floor(seconds), 3) * 10 ** 3)
seconds = floor(seconds)
return f'{hours:02}:{minutes:02}:{seconds:02}.{m_seconds:03}'
return f"[{_format_time(start)}-> {_format_time(end)}]:"
@spaces.GPU
def get_prediction(inputs, task: str, language: Optional[str]):
generate_kwargs = {"task": task, "language": language}
prediction = pipe(inputs, return_timestamps=True, generate_kwargs=generate_kwargs)
text = "".join([c['text'] for c in prediction['chunks']])
text_timestamped = "\n".join([
f"{format_time(*c['timestamp'])} {c['text']}" for c in prediction['chunks']
])
return text, text_timestamped
def transcribe(inputs: str, task: str, language: str):
if inputs is None:
raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
with open(inputs, "rb") as f:
inputs = f.read()
inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
return get_prediction(inputs, task, language)
demo = gr.Blocks()
description = (f"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses Kotoba-Whisper "
f"checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio"
f" files of arbitrary length.")
title = f"Transcribe Audio with {os.path.basename(MODEL_NAME)}"
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="microphone", type="filepath"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Radio(["ja", "en"], label="Output Language", value="ja")
],
outputs=["text", "text"],
title=title,
description=description,
allow_flagging="never",
)
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(sources="upload", type="filepath", label="Audio file"),
gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
gr.Radio(["ja", "en"], label="Output Language", value="ja")
],
outputs=["text", "text"],
title=title,
description=description,
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False, show_error=True)
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