import json
import os
import shutil

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download

from funasr.utils.compute_wer import compute_wer

def modelscope_infer_after_finetune(params):
    # prepare for decoding

    try:
        pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
    except BaseException:
        raise BaseException(f"Please download pretrain model from ModelScope firstly.")
    shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
    decoding_path = os.path.join(params["output_dir"], "decode_results")
    if os.path.exists(decoding_path):
        shutil.rmtree(decoding_path)
    os.mkdir(decoding_path)

    # decoding
    inference_pipeline = pipeline(
        task=Tasks.auto_speech_recognition,
        model=pretrained_model_path,
        output_dir=decoding_path,
        batch_size=params["batch_size"]
    )
    audio_in = os.path.join(params["data_dir"], "wav.scp")
    inference_pipeline(audio_in=audio_in)

    # computer CER if GT text is set
    text_in = os.path.join(params["data_dir"], "text")
    if os.path.exists(text_in):
        text_proc_file = os.path.join(decoding_path, "1best_recog/text")
        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))


if __name__ == '__main__':
    params = {}
    params["modelscope_model_name"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
    params["output_dir"] = "./checkpoint"
    params["data_dir"] = "./data/test"
    params["decoding_model_name"] = "valid.acc.ave_10best.pb"
    params["batch_size"] = 64
    modelscope_infer_after_finetune(params)