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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import logging
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from funasr.download.download_from_hub import download_model
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from funasr.train_utils.set_all_random_seed import set_all_random_seed
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from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_bytes
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from funasr.train_utils.device_funcs import to_device
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from tqdm import tqdm
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from funasr.train_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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from funasr.utils.register import registry_tables
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def build_iter_for_infer(data_in, input_len=None, data_type="sound"):
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"""
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:param input:
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:param input_len:
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:param data_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_path; 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]
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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; bytes
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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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@hydra.main(config_name=None, version_base=None)
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def main_hydra(kwargs: DictConfig):
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log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
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logging.basicConfig(level=log_level)
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import pdb;
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pdb.set_trace()
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model = AutoModel(**kwargs)
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res = model.generate(input=kwargs["input"])
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print(res)
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class AutoModel:
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def __init__(self, **kwargs):
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registry_tables.print()
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assert "model" in kwargs
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if "model_conf" not in kwargs:
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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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kwargs["batch_size"] = 1
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kwargs["device"] = device
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# build tokenizer
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tokenizer = kwargs.get("tokenizer", None)
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if tokenizer is not None:
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tokenizer_class = registry_tables.tokenizer_classes.get(tokenizer.lower())
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tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
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kwargs["tokenizer"] = tokenizer
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kwargs["token_list"] = tokenizer.token_list
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# build frontend
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frontend = kwargs.get("frontend", None)
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if frontend is not None:
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frontend_class = registry_tables.frontend_classes.get(frontend.lower())
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frontend = frontend_class(**kwargs["frontend_conf"])
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kwargs["frontend"] = frontend
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kwargs["input_size"] = frontend.output_size()
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# build model
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model_class = registry_tables.model_classes.get(kwargs["model"].lower())
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model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
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model.eval()
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model.to(device)
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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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self.kwargs = kwargs
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self.model = model
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self.tokenizer = tokenizer
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def generate(self, input, input_len=None, **cfg):
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self.kwargs.update(cfg)
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data_type = self.kwargs.get("data_type", "sound")
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batch_size = self.kwargs.get("batch_size", 1)
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if self.kwargs.get("device", "cpu") == "cpu":
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batch_size = 1
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key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
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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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if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
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batch["data_batch"] = data_batch[0]
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batch["data_lengths"] = input_len
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time1 = time.perf_counter()
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results, meta_data = self.model.generate(**batch, **self.kwargs)
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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.get("load_data", 0.0)
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speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
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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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if __name__ == '__main__':
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main_hydra()
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