File size: 3,657 Bytes
d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd 6422215 d23e0cd |
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 |
import torch
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
# Define model details
MODEL_NAME = "riteshkr/whisper-large-v3-quantized" # Update with your actual model ID
BATCH_SIZE = 8
# Select device based on availability of CUDA (GPU) or fallback to CPU
device = 0 if torch.cuda.is_available() else "cpu"
# Load the ASR model pipeline
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30, # Adjust as needed for your application
device=device,
)
# Utility function to format timestamps
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:
return seconds
# Transcription function for batch processing
def transcribe(files, task, return_timestamps):
transcriptions = []
for file in files: # Process each file in the batch
outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
text = outputs["text"]
if return_timestamps:
timestamps = outputs["chunks"]
formatted_chunks = [
f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
for chunk in timestamps
]
text = "\n".join(formatted_chunks)
transcriptions.append(text)
return "\n\n".join(transcriptions) # Return all transcriptions combined
# Define Gradio interface for microphone input
mic_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath", optional=True),
gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
gr.Checkbox(default=False, label="Return timestamps"),
],
outputs="text",
layout="horizontal",
title="Whisper Demo: Transcribe Audio",
description=(
f"Transcribe long-form microphone inputs with the {MODEL_NAME} model. Supports transcription and translation."
),
allow_flagging="never",
)
# Define Gradio interface for file upload
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="upload", type="filepath", label="Upload Audio File", optional=True),
gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
gr.Checkbox(default=False, label="Return timestamps"),
],
outputs="text",
layout="horizontal",
title="Whisper Demo: Transcribe Audio",
description=(
f"Upload audio files to transcribe or translate them using the {MODEL_NAME} model."
),
allow_flagging="never",
examples=[
["./example.flac", "transcribe", False],
["./example.flac", "transcribe", True],
],
)
# Create the Gradio tabbed interface for switching between modes
demo = gr.Blocks()
with demo:
gr.TabbedInterface(
[mic_transcribe, file_transcribe],
["Transcribe Microphone", "Transcribe Audio File"]
)
# Launch the app
if __name__ == "__main__":
demo.launch(debug=True, enable_queue=True, share=True)
|