app.py

import spaces
import torch
import gradio as gr
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import tempfile
import os

# Model configuration, this model contains synthetic data
MODEL_ID = "alakxender/whisper-small-dv-full"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
CHUNK_LENGTH_S = 10
STRIDE_LENGTH_S = [3,2]

# Device and dtype setup
device = 0 if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Initialize model with memory optimizations
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch_dtype,
    low_cpu_mem_usage=True,
    use_safetensors=True
)
model.to(device)

# Initialize processor
processor = AutoProcessor.from_pretrained(MODEL_ID)

# Single pipeline initialization with all components
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    chunk_length_s=CHUNK_LENGTH_S,
    stride_length_s=STRIDE_LENGTH_S,
    batch_size=BATCH_SIZE,
    torch_dtype=torch_dtype,
    device=device,
)

# Define the generation arguments

# Define optimized generation arguments
def get_generate_kwargs(is_short_audio=False):
    """
    Get appropriate generation parameters based on audio length.
    Short audio transcription benefits from different parameters.
    """
    common_kwargs = {
        "max_new_tokens": model.config.max_target_positions-4,
        "num_beams": 4,
        "condition_on_prev_tokens": False,
    }
    
    if is_short_audio:
        # Parameters optimized for short audio:
        return {
            **common_kwargs,
            "compression_ratio_threshold": 1.5,     # Balanced setting to avoid repetition
            "no_speech_threshold": 0.4,             # Higher threshold to reduce hallucinations
            "repetition_penalty": 1.5,              # Add penalty for repeated tokens
            "return_timestamps": True,              # Get timestamps for better segmentation
        }
    else:
        # Parameters for longer audio:
        return {
            **common_kwargs,
            "compression_ratio_threshold": 1.35,    # Standard compression ratio for longer audio
            "repetition_penalty": 1.2,              # Light penalty for repeated tokens
        }

# IMPORTANT: Fix for forced_decoder_ids error
# Remove forced_decoder_ids from the model's generation config
if hasattr(model.generation_config, 'forced_decoder_ids'):
    print("Removing forced_decoder_ids from generation config")
    model.generation_config.forced_decoder_ids = None

# Also check if it's in the model config
if hasattr(model.config, 'forced_decoder_ids'):
    print("Removing forced_decoder_ids from model config")
    delattr(model.config, 'forced_decoder_ids')
    
@spaces.GPU
def transcribe(audio_input):
    if audio_input is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    
    try:
        # Use the defined generate_kwargs dictionary
        result = pipe(
            audio_input,
            generate_kwargs=get_generate_kwargs()
        )
        return result["text"]
    except Exception as e:
        # More detailed error logging might be helpful here if issues persist
        print(f"Detailed Error: {e}") 
        raise gr.Error(f"Transcription failed: {str(e)}")

# Custom CSS with modern Gradio styling
custom_css = """
.thaana-textbox textarea {
    font-size: 18px !important;
    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma', 'Noto Sans Thaana', 'MV Boli' !important;
    line-height: 1.8 !important;
    direction: rtl !important;
}
"""

demo = gr.Blocks(css=custom_css)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources=["upload", "microphone"], type="filepath", label="Audio file"),
    ],
    outputs=gr.Textbox(
        label="",
        lines=2,
        elem_classes=["thaana-textbox"],
        rtl=True
    ),
    title="Transcribe Dhivehi Audio",
    description=(
        "Upload an audio file or record using your microphone to transcribe."
    ),
    flagging_mode="never",
    examples=[
        ["sample.mp3"]  
    ],
    api_name=False,
    cache_examples=False
)

with demo:
    gr.TabbedInterface([file_transcribe], ["Audio file"])

demo.queue().launch()