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import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
import librosa
MODEL_NAME = "EwoutLagendijk/whisper-small-indonesian"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
# Load model and processor
model_name = "EwoutLagendijk/whisper-small-indonesian"
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name)
# Update the generation config for transcription
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="id", task="transcribe")
def transcribe_speech(filepath):
# Load the audio
audio, sampling_rate = librosa.load(filepath, sr=16000)
# Define chunk size (e.g., 30 seconds)
chunk_duration = 30 # in seconds
chunk_samples = chunk_duration * sampling_rate
# Process audio in chunks
transcription = []
for i in range(0, len(audio), chunk_samples):
chunk = audio[i:i + chunk_samples]
# Convert the chunk into input features
inputs = processor(audio=chunk, sampling_rate=16000, return_tensors="pt").input_features
# Generate transcription for the chunk
generated_ids = model.generate(
inputs,
max_new_tokens=444, # Max allowed by Whisper
forced_decoder_ids=processor.get_decoder_prompt_ids(language="id", task="transcribe"),
return_timestamps = True
)
# Decode and append the transcription
chunk_transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
transcription.append(chunk_transcription)
# Combine all chunk transcriptions into a single string
return " ".join(transcription)
demo = gr.Blocks()
mic_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=gr.components.Textbox(),
)
file_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=gr.components.Textbox(),
)
with demo:
gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
demo.launch(debug=True) |