import gradio as gr
import numpy as np

import onnx_asr

models = {
    name: onnx_asr.load_model(name)
    for name in [
        "gigaam-v2-ctc",
        "gigaam-v2-rnnt",
        "nemo-fastconformer-ru-ctc",
        "nemo-fastconformer-ru-rnnt",
        "alphacep/vosk-model-ru",
        "alphacep/vosk-model-small-ru",
        "whisper-base",
    ]
}


def recoginize(audio: tuple[int, np.ndarray]):
    sample_rate, waveform = audio
    waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1)
    return [[name, model.recognize(waveform, sample_rate=sample_rate, language="ru")] for name, model in models.items()]


demo = gr.Interface(
    fn=recoginize,
    title="ASR demo using onnx-asr (Russian models)",
    description="""# Automatic Speech Recognition in Python using ONNX models - [onnx-asr](https://github.com/istupakov/onnx-asr)
    ## Models used in demo:
    * `gigaam-v2-ctc` - Sber GigaAM v2 CTC ([origin](https://github.com/salute-developers/GigaAM), [onnx](https://huggingface.co/istupakov/gigaam-v2-onnx))
    * `gigaam-v2-rnnt` - Sber GigaAM v2 RNN-T ([origin](https://github.com/salute-developers/GigaAM), [onnx](https://huggingface.co/istupakov/gigaam-v2-onnx))
    * `nemo-fastconformer-ru-ctc` - Nvidia FastConformer-Hybrid Large (ru) with CTC decoder ([origin](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc), [onnx](https://huggingface.co/istupakov/stt_ru_fastconformer_hybrid_large_pc_onnx))
    * `nemo-fastconformer-ru-rnnt` - Nvidia FastConformer-Hybrid Large (ru) with RNN-T decoder ([origin](https://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc), [onnx](https://huggingface.co/istupakov/stt_ru_fastconformer_hybrid_large_pc_onnx))
    * `alphacep/vosk-model-ru` - Alpha Cephei Vosk 0.54-ru ([origin](https://huggingface.co/alphacep/vosk-model-ru))
    * `alphacep/vosk-model-small-ru` - Alpha Cephei Vosk 0.52-small-ru ([origin](https://huggingface.co/alphacep/vosk-model-small-ru))
    * `whisper-base` - OpenAI Whisper Base exported with onnxruntime ([origin](https://huggingface.co/openai/whisper-base), [onnx](https://huggingface.co/istupakov/whisper-base-onnx))
    """,
    inputs=[gr.Audio(min_length=1, max_length=10)],
    outputs=[gr.Dataframe(headers=["Model", "result"], wrap=True, show_fullscreen_button=True)],
    flagging_mode="never",
)

demo.launch()