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import torch | |
from transformers import pipeline | |
import gradio as gr | |
MODEL_NAME = "openai/whisper-small" | |
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, 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 | |
demo = gr.Blocks() | |
mic_transcribe = gr.Interface( | |
fn=transcribe, | |
inputs=[ | |
gr.Audio(sources="microphone", type="filepath"), | |
gr.Radio(["transcribe", "translate"],value="transcribe",label="Task"), | |
gr.Checkbox(value=False, label="Return timestamps"), | |
], | |
outputs="text", | |
title="Whisper English Speech Transcription and Translation", | |
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 π€ 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"],value="transcribe",label="Task"), | |
gr.Checkbox(value=False, label="Return timestamps"), | |
], | |
outputs="text", | |
title="Whisper English Speech Transcription and Translation", | |
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 π€ Transformers to transcribe audio files" | |
" of arbitrary length." | |
), | |
examples=[ | |
["./example1.flac","transcribe", True], | |
["./example2.flac","translate", True], | |
], | |
cache_examples=True, | |
allow_flagging="never", | |
) | |
with demo: | |
gr.TabbedInterface([file_transcribe, mic_transcribe], ["Transcribe Audio File", "Transcribe Microphone"]) | |
# demo.queue() | |
demo.launch() |