From c0e72dd1ba86c19205ee633673b2497d18a68077 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 11 一月 2024 17:36:59 +0800
Subject: [PATCH] Merge branch 'funasr1.0' of github.com:alibaba-damo-academy/FunASR into funasr1.0 add
---
funasr/bin/inference.py | 880 ++++++++++++++++++++++++++++++---------------------------
1 files changed, 464 insertions(+), 416 deletions(-)
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index 2d94e70..cf29d91 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -1,453 +1,501 @@
-import os.path
-
-import torch
-import numpy as np
-import hydra
import json
-from omegaconf import DictConfig, OmegaConf, ListConfig
-import logging
-from funasr.download.download_from_hub import download_model
-from funasr.train_utils.set_all_random_seed import set_all_random_seed
-from funasr.utils.load_utils import load_bytes
-from funasr.train_utils.device_funcs import to_device
-from tqdm import tqdm
-from funasr.train_utils.load_pretrained_model import load_pretrained_model
import time
+import torch
+import hydra
import random
import string
-from funasr.register import tables
+import logging
+import os.path
+from tqdm import tqdm
+from omegaconf import DictConfig, OmegaConf, ListConfig
-from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-from funasr.utils.vad_utils import slice_padding_audio_samples
-from funasr.utils.timestamp_tools import time_stamp_sentence
+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
+
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
- """
-
- :param input:
- :param input_len:
- :param data_type:
- :param frontend:
- :return:
- """
- data_list = []
- key_list = []
- filelist = [".scp", ".txt", ".json", ".jsonl"]
-
- chars = string.ascii_letters + string.digits
- if isinstance(data_in, str) and data_in.startswith('http'): # url
- data_in = download_from_url(data_in)
- if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
- _, file_extension = os.path.splitext(data_in)
- file_extension = file_extension.lower()
- if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
- with open(data_in, encoding='utf-8') as fin:
- for line in fin:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
- lines = json.loads(line.strip())
- data = lines["source"]
- key = data["key"] if "key" in data else key
- else: # filelist, wav.scp, text.txt: id \t data or data
- lines = line.strip().split(maxsplit=1)
- data = lines[1] if len(lines)>1 else lines[0]
- key = lines[0] if len(lines)>1 else key
-
- data_list.append(data)
- key_list.append(key)
- else:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- data_list = [data_in]
- key_list = [key]
- elif isinstance(data_in, (list, tuple)):
- if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
- data_list_tmp = []
- for data_in_i, data_type_i in zip(data_in, data_type):
- key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
- data_list_tmp.append(data_list_i)
- data_list = []
- for item in zip(*data_list_tmp):
- data_list.append(item)
- else:
- # [audio sample point, fbank, text]
- data_list = data_in
- key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
- else: # raw text; audio sample point, fbank; bytes
- if isinstance(data_in, bytes): # audio bytes
- data_in = load_bytes(data_in)
- if key is None:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- data_list = [data_in]
- key_list = [key]
-
- return key_list, data_list
+ """
+
+ :param input:
+ :param input_len:
+ :param data_type:
+ :param frontend:
+ :return:
+ """
+ data_list = []
+ key_list = []
+ filelist = [".scp", ".txt", ".json", ".jsonl"]
+
+ chars = string.ascii_letters + string.digits
+ if isinstance(data_in, str) and data_in.startswith('http'): # url
+ data_in = download_from_url(data_in)
+ if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
+ _, file_extension = os.path.splitext(data_in)
+ file_extension = file_extension.lower()
+ if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
+ with open(data_in, encoding='utf-8') as fin:
+ for line in fin:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
+ lines = json.loads(line.strip())
+ data = lines["source"]
+ key = data["key"] if "key" in data else key
+ else: # filelist, wav.scp, text.txt: id \t data or data
+ lines = line.strip().split(maxsplit=1)
+ data = lines[1] if len(lines)>1 else lines[0]
+ key = lines[0] if len(lines)>1 else key
+
+ data_list.append(data)
+ key_list.append(key)
+ else:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+ elif isinstance(data_in, (list, tuple)):
+ if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
+ data_list_tmp = []
+ for data_in_i, data_type_i in zip(data_in, data_type):
+ key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
+ data_list_tmp.append(data_list_i)
+ data_list = []
+ for item in zip(*data_list_tmp):
+ data_list.append(item)
+ else:
+ # [audio sample point, fbank, text]
+ data_list = data_in
+ key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
+ else: # raw text; audio sample point, fbank; bytes
+ if isinstance(data_in, bytes): # audio bytes
+ data_in = load_bytes(data_in)
+ if key is None:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+
+ return key_list, data_list
@hydra.main(config_name=None, version_base=None)
def main_hydra(cfg: DictConfig):
- def to_plain_list(cfg_item):
- if isinstance(cfg_item, ListConfig):
- return OmegaConf.to_container(cfg_item, resolve=True)
- elif isinstance(cfg_item, DictConfig):
- return {k: to_plain_list(v) for k, v in cfg_item.items()}
- else:
- return cfg_item
-
- kwargs = to_plain_list(cfg)
- log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+ def to_plain_list(cfg_item):
+ if isinstance(cfg_item, ListConfig):
+ return OmegaConf.to_container(cfg_item, resolve=True)
+ elif isinstance(cfg_item, DictConfig):
+ return {k: to_plain_list(v) for k, v in cfg_item.items()}
+ else:
+ return cfg_item
+
+ kwargs = to_plain_list(cfg)
+ log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
- logging.basicConfig(level=log_level)
+ logging.basicConfig(level=log_level)
- if kwargs.get("debug", False):
- import pdb; pdb.set_trace()
- model = AutoModel(**kwargs)
- res = model(input=kwargs["input"])
- print(res)
+ if kwargs.get("debug", False):
+ import pdb; pdb.set_trace()
+ model = AutoModel(**kwargs)
+ res = model(input=kwargs["input"])
+ print(res)
class AutoModel:
-
- def __init__(self, **kwargs):
- tables.print()
-
- model, kwargs = self.build_model(**kwargs)
-
- # if vad_model is not None, build vad model else None
- vad_model = kwargs.get("vad_model", None)
- vad_kwargs = kwargs.get("vad_model_revision", None)
- if vad_model is not None:
- print("build vad model")
- vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
- vad_model, vad_kwargs = self.build_model(**vad_kwargs)
+
+ def __init__(self, **kwargs):
+ tables.print()
+
+ model, kwargs = self.build_model(**kwargs)
+
+ # if vad_model is not None, build vad model else None
+ vad_model = kwargs.get("vad_model", None)
+ vad_kwargs = kwargs.get("vad_model_revision", None)
+ if vad_model is not None:
+ print("build vad model")
+ vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+ vad_model, vad_kwargs = self.build_model(**vad_kwargs)
- # if punc_model is not None, build punc model else None
- punc_model = kwargs.get("punc_model", None)
- punc_kwargs = kwargs.get("punc_model_revision", None)
- if punc_model is not None:
- punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
- punc_model, punc_kwargs = self.build_model(**punc_kwargs)
-
- self.kwargs = kwargs
- self.model = model
- self.vad_model = vad_model
- self.vad_kwargs = vad_kwargs
- self.punc_model = punc_model
- self.punc_kwargs = punc_kwargs
-
-
+ # if punc_model is not None, build punc model else None
+ punc_model = kwargs.get("punc_model", None)
+ punc_kwargs = kwargs.get("punc_model_revision", None)
+ if punc_model is not None:
+ punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+ punc_model, punc_kwargs = self.build_model(**punc_kwargs)
- def build_model(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)
-
- set_all_random_seed(kwargs.get("seed", 0))
-
- device = kwargs.get("device", "cuda")
- if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
- device = "cpu"
- # kwargs["batch_size"] = 1
- kwargs["device"] = device
-
- if kwargs.get("ncpu", None):
- torch.set_num_threads(kwargs.get("ncpu"))
-
- # build tokenizer
- tokenizer = kwargs.get("tokenizer", None)
- if tokenizer is not None:
- tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
- tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
- kwargs["tokenizer"] = tokenizer
- kwargs["token_list"] = tokenizer.token_list
- vocab_size = len(tokenizer.token_list)
- else:
- vocab_size = -1
-
- # build frontend
- frontend = kwargs.get("frontend", None)
- if frontend is not None:
- frontend_class = tables.frontend_classes.get(frontend.lower())
- frontend = frontend_class(**kwargs["frontend_conf"])
- kwargs["frontend"] = frontend
- kwargs["input_size"] = frontend.output_size()
-
- # build model
- model_class = tables.model_classes.get(kwargs["model"].lower())
- model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
- model.eval()
- model.to(device)
-
- # init_param
- init_param = kwargs.get("init_param", None)
- if init_param is not None:
- logging.info(f"Loading pretrained params from {init_param}")
- load_pretrained_model(
- model=model,
- init_param=init_param,
- ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
- oss_bucket=kwargs.get("oss_bucket", None),
- )
-
- return model, kwargs
-
- def __call__(self, input, input_len=None, **cfg):
- if self.vad_model is None:
- return self.generate(input, input_len=input_len, **cfg)
-
- else:
- return self.generate_with_vad(input, input_len=input_len, **cfg)
-
- def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
- # import pdb; pdb.set_trace()
- kwargs = self.kwargs if kwargs is None else kwargs
- kwargs.update(cfg)
- model = self.model if model is None else model
+ # if spk_model is not None, build spk model else None
+ spk_model = kwargs.get("spk_model", None)
+ spk_kwargs = kwargs.get("spk_model_revision", None)
+ if spk_model is not None:
+ spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
+ spk_model, spk_kwargs = self.build_model(**spk_kwargs)
+ self.cb_model = ClusterBackend()
+ spk_mode = kwargs.get("spk_mode", 'punc_segment')
+ if spk_mode not in ["default", "vad_segment", "punc_segment"]:
+ logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
+ self.spk_mode = spk_mode
+ logging.warning("Many to print when using speaker model...")
+
+ self.kwargs = kwargs
+ self.model = model
+ self.vad_model = vad_model
+ self.vad_kwargs = vad_kwargs
+ self.punc_model = punc_model
+ self.punc_kwargs = punc_kwargs
+ self.spk_model = spk_model
+ self.spk_kwargs = spk_kwargs
+
+
+ def build_model(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)
+
+ set_all_random_seed(kwargs.get("seed", 0))
+
+ device = kwargs.get("device", "cuda")
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
+ device = "cpu"
+ # kwargs["batch_size"] = 1
+ kwargs["device"] = device
+
+ if kwargs.get("ncpu", None):
+ torch.set_num_threads(kwargs.get("ncpu"))
+
+ # build tokenizer
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+ kwargs["token_list"] = tokenizer.token_list
+ vocab_size = len(tokenizer.token_list)
+ else:
+ vocab_size = -1
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend.lower())
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"].lower())
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+ model.eval()
+ model.to(device)
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ logging.info(f"Loading pretrained params from {init_param}")
+ load_pretrained_model(
+ model=model,
+ init_param=init_param,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ )
+
+ return model, kwargs
+
+ def __call__(self, input, input_len=None, **cfg):
+ if self.vad_model is None:
+ return self.generate(input, input_len=input_len, **cfg)
+
+ else:
+ return self.generate_with_vad(input, input_len=input_len, **cfg)
+
+ def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+ model = self.model if model is None else model
- batch_size = kwargs.get("batch_size", 1)
- # if kwargs.get("device", "cpu") == "cpu":
- # batch_size = 1
-
- key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
-
- speed_stats = {}
- asr_result_list = []
- num_samples = len(data_list)
- pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
- time_speech_total = 0.0
- time_escape_total = 0.0
- 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]
- batch = {"data_in": data_batch, "key": key_batch}
- if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
- batch["data_in"] = data_batch[0]
- batch["data_lengths"] = input_len
-
- time1 = time.perf_counter()
- with torch.no_grad():
- results, meta_data = model.generate(**batch, **kwargs)
- time2 = time.perf_counter()
-
- asr_result_list.extend(results)
- pbar.update(1)
-
- # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
- batch_data_time = meta_data.get("batch_data_time", -1)
- time_escape = time2 - time1
- speed_stats["load_data"] = meta_data.get("load_data", 0.0)
- speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
- speed_stats["forward"] = f"{time_escape:0.3f}"
- speed_stats["batch_size"] = f"{len(results)}"
- speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
- description = (
- f"{speed_stats}, "
- )
- pbar.set_description(description)
- time_speech_total += batch_data_time
- time_escape_total += time_escape
-
- pbar.update(1)
- pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
- torch.cuda.empty_cache()
- return asr_result_list
-
- def generate_with_vad(self, input, input_len=None, **cfg):
-
- # step.1: compute the vad model
- model = self.vad_model
- kwargs = self.vad_kwargs
- kwargs.update(cfg)
- beg_vad = time.time()
- res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg)
- end_vad = time.time()
- print(f"time cost vad: {end_vad - beg_vad:0.3f}")
+ batch_size = kwargs.get("batch_size", 1)
+ # if kwargs.get("device", "cpu") == "cpu":
+ # batch_size = 1
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
+
+ speed_stats = {}
+ asr_result_list = []
+ num_samples = len(data_list)
+ pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
+ time_speech_total = 0.0
+ time_escape_total = 0.0
+ 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]
+ batch = {"data_in": data_batch, "key": key_batch}
+ if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
+ batch["data_in"] = data_batch[0]
+ batch["data_lengths"] = input_len
+
+ time1 = time.perf_counter()
+ with torch.no_grad():
+ results, meta_data = model.generate(**batch, **kwargs)
+ time2 = time.perf_counter()
+
+ asr_result_list.extend(results)
+ pbar.update(1)
+
+ # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
+ batch_data_time = meta_data.get("batch_data_time", -1)
+ time_escape = time2 - time1
+ speed_stats["load_data"] = meta_data.get("load_data", 0.0)
+ speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
+ speed_stats["forward"] = f"{time_escape:0.3f}"
+ speed_stats["batch_size"] = f"{len(results)}"
+ speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
+ description = (
+ f"{speed_stats}, "
+ )
+ pbar.set_description(description)
+ time_speech_total += batch_data_time
+ time_escape_total += time_escape
+
+ pbar.update(1)
+ pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
+ torch.cuda.empty_cache()
+ return asr_result_list
+
+ def generate_with_vad(self, input, input_len=None, **cfg):
+
+ # step.1: compute the vad model
+ model = self.vad_model
+ kwargs = self.vad_kwargs
+ kwargs.update(cfg)
+ beg_vad = time.time()
+ res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg)
+ vad_res = res
+ end_vad = time.time()
+ print(f"time cost vad: {end_vad - beg_vad:0.3f}")
- # step.2 compute asr model
- model = self.model
- kwargs = self.kwargs
- kwargs.update(cfg)
- batch_size = int(kwargs.get("batch_size_s", 300))*1000
- batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
- kwargs["batch_size"] = batch_size
-
- key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
- results_ret_list = []
- time_speech_total_all_samples = 0.0
+ # step.2 compute asr model
+ model = self.model
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ batch_size = int(kwargs.get("batch_size_s", 300))*1000
+ batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
+ kwargs["batch_size"] = batch_size
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
+ results_ret_list = []
+ time_speech_total_all_samples = 0.0
- beg_total = time.time()
- pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
- for i in range(len(res)):
- key = res[i]["key"]
- vadsegments = res[i]["value"]
- input_i = data_list[i]
- speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
- speech_lengths = len(speech)
- n = len(vadsegments)
- data_with_index = [(vadsegments[i], i) for i in range(n)]
- sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
- results_sorted = []
-
- if not len(sorted_data):
- logging.info("decoding, utt: {}, empty speech".format(key))
- continue
-
+ beg_total = time.time()
+ pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+ for i in range(len(res)):
+ key = res[i]["key"]
+ vadsegments = res[i]["value"]
+ input_i = data_list[i]
+ speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
+ speech_lengths = len(speech)
+ n = len(vadsegments)
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+ results_sorted = []
+
+ if not len(sorted_data):
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
- # if kwargs["device"] == "cpu":
- # batch_size = 0
- if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
- batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
-
- batch_size_ms_cum = 0
- beg_idx = 0
- beg_asr_total = time.time()
- time_speech_total_per_sample = speech_lengths/16000
- time_speech_total_all_samples += time_speech_total_per_sample
+ # if kwargs["device"] == "cpu":
+ # batch_size = 0
+ if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+ batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
+
+ batch_size_ms_cum = 0
+ beg_idx = 0
+ beg_asr_total = time.time()
+ time_speech_total_per_sample = speech_lengths/16000
+ time_speech_total_all_samples += time_speech_total_per_sample
- for j, _ in enumerate(range(0, n)):
- batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
- if j < n - 1 and (
- batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
- sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
- continue
- batch_size_ms_cum = 0
- end_idx = j + 1
- speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
- beg_idx = end_idx
-
- results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
-
- if len(results) < 1:
- continue
- results_sorted.extend(results)
+ for j, _ in enumerate(range(0, n)):
+ batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
+ if j < n - 1 and (
+ batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
+ sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
+ continue
+ batch_size_ms_cum = 0
+ end_idx = j + 1
+ speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
+ results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+ if self.spk_model is not None:
+ all_segments = []
+ # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
+ for _b in range(len(speech_j)):
+ vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
+ sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
+ speech_j[_b]]]
+ segments = sv_chunk(vad_segments)
+ all_segments.extend(segments)
+ speech_b = [i[2] for i in segments]
+ spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+ results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
+ beg_idx = end_idx
+ if len(results) < 1:
+ continue
+ results_sorted.extend(results)
- pbar_total.update(1)
- end_asr_total = time.time()
- time_escape_total_per_sample = end_asr_total - beg_asr_total
- pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
- f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
- f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+ pbar_total.update(1)
+ end_asr_total = time.time()
+ time_escape_total_per_sample = end_asr_total - beg_asr_total
+ pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
+ f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
- restored_data = [0] * n
- for j in range(n):
- index = sorted_data[j][1]
- restored_data[index] = results_sorted[j]
- result = {}
-
- for j in range(n):
- for k, v in restored_data[j].items():
- if not k.startswith("timestamp"):
- if k not in result:
- result[k] = restored_data[j][k]
- else:
- result[k] += restored_data[j][k]
- else:
- result[k] = []
- for t in restored_data[j][k]:
- t[0] += vadsegments[j][0]
- t[1] += vadsegments[j][0]
- result[k] += restored_data[j][k]
-
- result["key"] = key
- results_ret_list.append(result)
- pbar_total.update(1)
-
- # step.3 compute punc model
- model = self.punc_model
- kwargs = self.punc_kwargs
- kwargs.update(cfg)
-
- for i, result in enumerate(results_ret_list):
- beg_punc = time.time()
- res = self.generate(result["text"], model=model, kwargs=kwargs, **cfg)
- end_punc = time.time()
- print(f"time punc: {end_punc - beg_punc:0.3f}")
-
- # sentences = time_stamp_sentence(model.punc_list, model.sentence_end_id, results_ret_list[i]["timestamp"], res[i]["text"])
- # results_ret_list[i]["time_stamp"] = res[0]["text_postprocessed_punc"]
- # results_ret_list[i]["sentences"] = sentences
- results_ret_list[i]["text_with_punc"] = res[i]["text"]
-
- pbar_total.update(1)
- end_total = time.time()
- time_escape_total_all_samples = end_total - beg_total
- pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
- f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
- f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
- return results_ret_list
+ restored_data = [0] * n
+ for j in range(n):
+ index = sorted_data[j][1]
+ restored_data[index] = results_sorted[j]
+ result = {}
+
+ # results combine for texts, timestamps, speaker embeddings and others
+ # TODO: rewrite for clean code
+ for j in range(n):
+ for k, v in restored_data[j].items():
+ if k.startswith("timestamp"):
+ if k not in result:
+ result[k] = []
+ for t in restored_data[j][k]:
+ t[0] += vadsegments[j][0]
+ t[1] += vadsegments[j][0]
+ result[k].extend(restored_data[j][k])
+ elif k == 'spk_embedding':
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
+ elif k == 'text':
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += " " + restored_data[j][k]
+ else:
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += restored_data[j][k]
+
+ # step.3 compute punc model
+ if self.punc_model is not None:
+ self.punc_kwargs.update(cfg)
+ punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+ result["text_with_punc"] = punc_res[0]["text"]
+
+ # speaker embedding cluster after resorted
+ if self.spk_model is not None:
+ all_segments = sorted(all_segments, key=lambda x: x[0])
+ spk_embedding = result['spk_embedding']
+ labels = self.cb_model(spk_embedding)
+ del result['spk_embedding']
+ sv_output = postprocess(all_segments, None, labels, spk_embedding)
+ if self.spk_mode == 'vad_segment':
+ sentence_list = []
+ for res, vadsegment in zip(restored_data, vadsegments):
+ sentence_list.append({"start": vadsegment[0],\
+ "end": vadsegment[1],
+ "sentence": res['text'],
+ "timestamp": res['timestamp']})
+ else: # punc_segment
+ sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+ result['timestamp'], \
+ result['text'])
+ distribute_spk(sentence_list, sv_output)
+ result['sentence_info'] = sentence_list
+
+ result["key"] = key
+ results_ret_list.append(result)
+ pbar_total.update(1)
+
+ pbar_total.update(1)
+ end_total = time.time()
+ time_escape_total_all_samples = end_total - beg_total
+ pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+ f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
+ f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
+ return results_ret_list
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.lower())
- frontend = frontend_class(**kwargs["frontend_conf"])
+ 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.lower())
+ frontend = frontend_class(**kwargs["frontend_conf"])
- self.frontend = frontend
- if "frontend" in kwargs:
- del kwargs["frontend"]
- self.kwargs = kwargs
+ 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)
+
+ 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]
+ 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
+ # 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
if __name__ == '__main__':
- main_hydra()
\ No newline at end of file
+ main_hydra()
\ No newline at end of file
--
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