import os.path
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import torch
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import numpy as np
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import hydra
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import json
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from omegaconf import DictConfig, OmegaConf
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from funasr.utils.dynamic_import import dynamic_import
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import logging
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from funasr.utils.download_from_hub import download_model
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.tokenizer.funtoken import build_tokenizer
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from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_bytes
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from funasr.torch_utils.device_funcs import to_device
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from tqdm import tqdm
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from funasr.torch_utils.load_pretrained_model import load_pretrained_model
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import time
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import random
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import string
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@hydra.main(config_name=None, version_base=None)
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def main_hydra(kwargs: DictConfig):
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assert "model" in kwargs
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pipeline = infer(**kwargs)
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res = pipeline(input=kwargs["input"])
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print(res)
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def infer(**kwargs):
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if ":" not in kwargs["model"]:
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logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
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kwargs = download_model(**kwargs)
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set_all_random_seed(kwargs.get("seed", 0))
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device = kwargs.get("device", "cuda")
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if not torch.cuda.is_available() or kwargs.get("ngpu", 1):
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device = "cpu"
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batch_size = 1
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kwargs["device"] = device
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# build_tokenizer
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tokenizer = build_tokenizer(
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token_type=kwargs.get("token_type", "char"),
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bpemodel=kwargs.get("bpemodel", None),
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delimiter=kwargs.get("delimiter", None),
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space_symbol=kwargs.get("space_symbol", "<space>"),
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non_linguistic_symbols=kwargs.get("non_linguistic_symbols", None),
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g2p_type=kwargs.get("g2p_type", None),
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token_list=kwargs.get("token_list", None),
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unk_symbol=kwargs.get("unk_symbol", "<unk>"),
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)
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import pdb;
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pdb.set_trace()
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# build model
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model_class = dynamic_import(kwargs.get("model"))
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model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
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model.eval()
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model.to(device)
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frontend = model.frontend
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kwargs["token_list"] = tokenizer.token_list
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# init_param
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init_param = kwargs.get("init_param", None)
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if init_param is not None:
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logging.info(f"Loading pretrained params from {init_param}")
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load_pretrained_model(
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model=model,
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init_param=init_param,
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ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
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oss_bucket=kwargs.get("oss_bucket", None),
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)
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def _forward(input, input_len=None, **cfg):
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cfg = OmegaConf.merge(kwargs, cfg)
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date_type = cfg.get("date_type", "sound")
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key_list, data_list = build_iter_for_infer(input, input_len=input_len, date_type=date_type, frontend=frontend)
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speed_stats = {}
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asr_result_list = []
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num_samples = len(data_list)
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pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
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for beg_idx in range(0, num_samples, batch_size):
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end_idx = min(num_samples, beg_idx + batch_size)
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data_batch = data_list[beg_idx:end_idx]
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key_batch = key_list[beg_idx:end_idx]
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batch = {"data_in": data_batch, "key": key_batch}
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time1 = time.perf_counter()
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results, meta_data = model.generate(**batch, tokenizer=tokenizer, **cfg)
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time2 = time.perf_counter()
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asr_result_list.append(results)
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pbar.update(1)
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# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
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batch_data_time = meta_data.get("batch_data_time", -1)
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speed_stats["load_data"] = meta_data["load_data"]
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speed_stats["extract_feat"] = meta_data["extract_feat"]
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speed_stats["forward"] = f"{time2 - time1:0.3f}"
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speed_stats["rtf"] = f"{(time2 - time1)/batch_data_time:0.3f}"
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description = (
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f"{speed_stats}, "
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)
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pbar.set_description(description)
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torch.cuda.empty_cache()
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return asr_result_list
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return _forward
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def build_iter_for_infer(data_in, input_len=None, date_type="sound", frontend=None):
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"""
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:param input:
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:param input_len:
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:param date_type:
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:param frontend:
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:return:
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"""
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data_list = []
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key_list = []
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filelist = [".scp", ".txt", ".json", ".jsonl"]
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chars = string.ascii_letters + string.digits
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if isinstance(data_in, str) and os.path.exists(data_in): # wav_pat; filelist: wav.scp, file.jsonl;text.txt;
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_, file_extension = os.path.splitext(data_in)
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file_extension = file_extension.lower()
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if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
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with open(data_in, encoding='utf-8') as fin:
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for line in fin:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
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lines = json.loads(line.strip())
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data = lines["source"]
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key = data["key"] if "key" in data else key
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else: # filelist, wav.scp, text.txt: id \t data or data
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lines = line.strip().split()
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data = lines[1] if len(lines)>1 else lines[0]
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key = lines[0] if len(lines)>1 else key
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data_list.append(data)
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key_list.append(key)
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else:
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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elif isinstance(data_in, (list, tuple)): # [audio sample point, fbank, wav_path]
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data_list = data_in
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key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
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else: # raw text; audio sample point, fbank
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if isinstance(data_in, bytes): # audio bytes
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data_in = load_bytes(data_in)
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key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
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data_list = [data_in]
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key_list = [key]
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return key_list, data_list
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if __name__ == '__main__':
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main_hydra()
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