FunASR/funasr/auto/auto_frontend.py

import json
import time
import torch
import hydra
import random
import string
import logging
import os.path
from tqdm import tqdm
from omegaconf import DictConfig, OmegaConf, ListConfig

from funasr.register import tables
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
from funasr.download.download_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
from funasr.models.campplus.cluster_backend import ClusterBackend
from funasr.auto.auto_model import prepare_data_iterator



class AutoFrontend:
    def __init__(self, **kwargs):
        assert "model" in kwargs
        if "model_conf" not in kwargs:
            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
            kwargs = download_model(**kwargs)
        
        # build frontend
        frontend = kwargs.get("frontend", None)
        if frontend is not None:
            frontend_class = tables.frontend_classes.get(frontend)
            frontend = frontend_class(**kwargs["frontend_conf"])

        self.frontend = frontend
        if "frontend" in kwargs:
            del kwargs["frontend"]
        self.kwargs = kwargs

    
    def __call__(self, input, input_len=None, kwargs=None, **cfg):
        
        kwargs = self.kwargs if kwargs is None else kwargs
        kwargs.update(cfg)


        key_list, data_list = prepare_data_iterator(input, input_len=input_len)
        batch_size = kwargs.get("batch_size", 1)
        device = kwargs.get("device", "cpu")
        if device == "cpu":
            batch_size = 1
        
        meta_data = {}
        
        result_list = []
        num_samples = len(data_list)
        pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
        
        time0 = time.perf_counter()
        for beg_idx in range(0, num_samples, batch_size):
            end_idx = min(num_samples, beg_idx + batch_size)
            data_batch = data_list[beg_idx:end_idx]
            key_batch = key_list[beg_idx:end_idx]

            # extract fbank feats
            time1 = time.perf_counter()
            audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
            time2 = time.perf_counter()
            meta_data["load_data"] = f"{time2 - time1:0.3f}"
            speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                                   frontend=self.frontend, **kwargs)
            time3 = time.perf_counter()
            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
            meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
            
            speech.to(device=device), speech_lengths.to(device=device)
            batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
            result_list.append(batch)
            
            pbar.update(1)
            description = (
                f"{meta_data}, "
            )
            pbar.set_description(description)
        
        time_end = time.perf_counter()
        pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
        
        return result_list