import logging

import gradio as gr

# import torch
from transformers import (
    AutoModelForSpeechSeq2Seq,
    AutoProcessor,
    pipeline,
    WhisperProcessor,
)


device = "cpu"
# device = "cuda:0" if torch.cuda.is_available() else "cpu"
# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-large-v3"

# model = AutoModelForSpeechSeq2Seq.from_pretrained(
#     model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
# )

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = WhisperProcessor.from_pretrained("openai/whisper-base.en")

# processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    task="automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    # max_new_tokens=128,
    chunk_length_s=30,
    batch_size=8,
    # return_timestamps=True,
    # torch_dtype=torch_dtype,
    device=device,
)


def transcribe_audio(audio):
    result = pipe(audio)
    logging.info(f'TRANSCRIPTION {result["text"]}')
    return result


input_audio = gr.Audio(
    source="microphone",
    type="filepath",
    optional=True,
    waveform_options=gr.WaveformOptions(
        waveform_color="#01C6FF",
        waveform_progress_color="#0066B4",
        skip_length=2,
        show_controls=False,
    ),
)
demo = gr.Interface(fn=transcribe_audio, inputs=input_audio, outputs="text")

if __name__ == "__main__":
    demo.launch()