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 +++++++++++++++++++-----------------
funasr/models/campplus/model.py | 10
funasr/utils/timestamp_tools.py | 209 --------
funasr/models/ct_transformer/model.py | 10
examples/industrial_data_pretraining/bicif_paraformer/demo.py | 28
funasr/models/campplus/cluster_backend.py | 191 +++++++
funasr/models/campplus/utils.py | 90 +-
7 files changed, 747 insertions(+), 671 deletions(-)
diff --git a/examples/industrial_data_pretraining/bicif_paraformer/demo.py b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
index 4a5e333..16eed37 100644
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
@@ -6,12 +6,28 @@
from funasr import AutoModel
model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
- model_revision="v2.0.0",
- vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.0",
- punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
- punc_model_revision="v2.0.0",
+ model_revision="v2.0.0",
+ vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+ vad_model_revision="v2.0.0",
+ punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+ punc_model_revision="v2.0.0",
+ spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
)
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
-print(res)
\ No newline at end of file
+print(res)
+
+'''try asr with speaker label with
+model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+ model_revision="v2.0.0",
+ vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+ vad_model_revision="v2.0.0",
+ punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+ punc_model_revision="v2.0.0",
+ spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
+ spk_mode='punc_segment',
+ )
+
+res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav", batch_size_s=300, batch_size_threshold_s=60)
+print(res)
+'''
\ No newline at end of file
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
diff --git a/funasr/models/campplus/cluster_backend.py b/funasr/models/campplus/cluster_backend.py
new file mode 100644
index 0000000..47b45d2
--- /dev/null
+++ b/funasr/models/campplus/cluster_backend.py
@@ -0,0 +1,191 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from typing import Any, Dict, Union
+
+import hdbscan
+import numpy as np
+import scipy
+import sklearn
+import umap
+from sklearn.cluster._kmeans import k_means
+from torch import nn
+
+
+class SpectralCluster:
+ r"""A spectral clustering mehtod using unnormalized Laplacian of affinity matrix.
+ This implementation is adapted from https://github.com/speechbrain/speechbrain.
+ """
+
+ def __init__(self, min_num_spks=1, max_num_spks=15, pval=0.022):
+ self.min_num_spks = min_num_spks
+ self.max_num_spks = max_num_spks
+ self.pval = pval
+
+ def __call__(self, X, oracle_num=None):
+ # Similarity matrix computation
+ sim_mat = self.get_sim_mat(X)
+
+ # Refining similarity matrix with pval
+ prunned_sim_mat = self.p_pruning(sim_mat)
+
+ # Symmetrization
+ sym_prund_sim_mat = 0.5 * (prunned_sim_mat + prunned_sim_mat.T)
+
+ # Laplacian calculation
+ laplacian = self.get_laplacian(sym_prund_sim_mat)
+
+ # Get Spectral Embeddings
+ emb, num_of_spk = self.get_spec_embs(laplacian, oracle_num)
+
+ # Perform clustering
+ labels = self.cluster_embs(emb, num_of_spk)
+
+ return labels
+
+ def get_sim_mat(self, X):
+ # Cosine similarities
+ M = sklearn.metrics.pairwise.cosine_similarity(X, X)
+ return M
+
+ def p_pruning(self, A):
+ if A.shape[0] * self.pval < 6:
+ pval = 6. / A.shape[0]
+ else:
+ pval = self.pval
+
+ n_elems = int((1 - pval) * A.shape[0])
+
+ # For each row in a affinity matrix
+ for i in range(A.shape[0]):
+ low_indexes = np.argsort(A[i, :])
+ low_indexes = low_indexes[0:n_elems]
+
+ # Replace smaller similarity values by 0s
+ A[i, low_indexes] = 0
+ return A
+
+ def get_laplacian(self, M):
+ M[np.diag_indices(M.shape[0])] = 0
+ D = np.sum(np.abs(M), axis=1)
+ D = np.diag(D)
+ L = D - M
+ return L
+
+ def get_spec_embs(self, L, k_oracle=None):
+ lambdas, eig_vecs = scipy.linalg.eigh(L)
+
+ if k_oracle is not None:
+ num_of_spk = k_oracle
+ else:
+ lambda_gap_list = self.getEigenGaps(
+ lambdas[self.min_num_spks - 1:self.max_num_spks + 1])
+ num_of_spk = np.argmax(lambda_gap_list) + self.min_num_spks
+
+ emb = eig_vecs[:, :num_of_spk]
+ return emb, num_of_spk
+
+ def cluster_embs(self, emb, k):
+ _, labels, _ = k_means(emb, k)
+ return labels
+
+ def getEigenGaps(self, eig_vals):
+ eig_vals_gap_list = []
+ for i in range(len(eig_vals) - 1):
+ gap = float(eig_vals[i + 1]) - float(eig_vals[i])
+ eig_vals_gap_list.append(gap)
+ return eig_vals_gap_list
+
+
+class UmapHdbscan:
+ r"""
+ Reference:
+ - Siqi Zheng, Hongbin Suo. Reformulating Speaker Diarization as Community Detection With
+ Emphasis On Topological Structure. ICASSP2022
+ """
+
+ def __init__(self,
+ n_neighbors=20,
+ n_components=60,
+ min_samples=10,
+ min_cluster_size=10,
+ metric='cosine'):
+ self.n_neighbors = n_neighbors
+ self.n_components = n_components
+ self.min_samples = min_samples
+ self.min_cluster_size = min_cluster_size
+ self.metric = metric
+
+ def __call__(self, X):
+ umap_X = umap.UMAP(
+ n_neighbors=self.n_neighbors,
+ min_dist=0.0,
+ n_components=min(self.n_components, X.shape[0] - 2),
+ metric=self.metric,
+ ).fit_transform(X)
+ labels = hdbscan.HDBSCAN(
+ min_samples=self.min_samples,
+ min_cluster_size=self.min_cluster_size,
+ allow_single_cluster=True).fit_predict(umap_X)
+ return labels
+
+
+class ClusterBackend(nn.Module):
+ r"""Perfom clustering for input embeddings and output the labels.
+ Args:
+ model_dir: A model dir.
+ model_config: The model config.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.model_config = {'merge_thr':0.78}
+ # self.other_config = kwargs
+
+ self.spectral_cluster = SpectralCluster()
+ self.umap_hdbscan_cluster = UmapHdbscan()
+
+ def forward(self, X, **params):
+ # clustering and return the labels
+ k = params['oracle_num'] if 'oracle_num' in params else None
+ assert len(
+ X.shape
+ ) == 2, 'modelscope error: the shape of input should be [N, C]'
+ if X.shape[0] < 20:
+ return np.zeros(X.shape[0], dtype='int')
+ if X.shape[0] < 2048 or k is not None:
+ labels = self.spectral_cluster(X, k)
+ else:
+ labels = self.umap_hdbscan_cluster(X)
+
+ if k is None and 'merge_thr' in self.model_config:
+ labels = self.merge_by_cos(labels, X,
+ self.model_config['merge_thr'])
+
+ return labels
+
+ def merge_by_cos(self, labels, embs, cos_thr):
+ # merge the similar speakers by cosine similarity
+ assert cos_thr > 0 and cos_thr <= 1
+ while True:
+ spk_num = labels.max() + 1
+ if spk_num == 1:
+ break
+ spk_center = []
+ for i in range(spk_num):
+ spk_emb = embs[labels == i].mean(0)
+ spk_center.append(spk_emb)
+ assert len(spk_center) > 0
+ spk_center = np.stack(spk_center, axis=0)
+ norm_spk_center = spk_center / np.linalg.norm(
+ spk_center, axis=1, keepdims=True)
+ affinity = np.matmul(norm_spk_center, norm_spk_center.T)
+ affinity = np.triu(affinity, 1)
+ spks = np.unravel_index(np.argmax(affinity), affinity.shape)
+ if affinity[spks] < cos_thr:
+ break
+ for i in range(len(labels)):
+ if labels[i] == spks[1]:
+ labels[i] = spks[0]
+ elif labels[i] > spks[1]:
+ labels[i] -= 1
+ return labels
diff --git a/funasr/models/campplus/model.py b/funasr/models/campplus/model.py
index 84938cc..7b1e098 100644
--- a/funasr/models/campplus/model.py
+++ b/funasr/models/campplus/model.py
@@ -109,13 +109,9 @@
audio_sample_list = load_audio_text_image_video(data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound")
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_feature(audio_sample_list)
+ speech, speech_lengths, speech_times = extract_feature(audio_sample_list)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = np.array(speech_lengths).sum().item() / 16000.0
- # import pdb; pdb.set_trace()
- results = []
- embeddings = self.forward(speech)
- for embedding in embeddings:
- results.append({"spk_embedding":embedding})
+ meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0
+ results = [{"spk_embedding": self.forward(speech)}]
return results, meta_data
\ No newline at end of file
diff --git a/funasr/models/campplus/utils.py b/funasr/models/campplus/utils.py
index c86a9f0..9964356 100644
--- a/funasr/models/campplus/utils.py
+++ b/funasr/models/campplus/utils.py
@@ -2,23 +2,19 @@
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import io
-from typing import Union
-
-import librosa as sf
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchaudio.compliance.kaldi as Kaldi
-from torch import nn
-
-import contextlib
import os
+import torch
+import requests
import tempfile
-from abc import ABCMeta, abstractmethod
+import contextlib
+import numpy as np
+import librosa as sf
+from typing import Union
from pathlib import Path
from typing import Generator, Union
-
-import requests
+from abc import ABCMeta, abstractmethod
+import torchaudio.compliance.kaldi as Kaldi
+from funasr.models.transformer.utils.nets_utils import pad_list
def check_audio_list(audio: list):
@@ -40,31 +36,31 @@
def sv_preprocess(inputs: Union[np.ndarray, list]):
- output = []
- for i in range(len(inputs)):
- if isinstance(inputs[i], str):
- file_bytes = File.read(inputs[i])
- data, fs = sf.load(io.BytesIO(file_bytes), dtype='float32')
- if len(data.shape) == 2:
- data = data[:, 0]
- data = torch.from_numpy(data).unsqueeze(0)
- data = data.squeeze(0)
- elif isinstance(inputs[i], np.ndarray):
- assert len(
- inputs[i].shape
- ) == 1, 'modelscope error: Input array should be [N, T]'
- data = inputs[i]
- if data.dtype in ['int16', 'int32', 'int64']:
- data = (data / (1 << 15)).astype('float32')
- else:
- data = data.astype('float32')
- data = torch.from_numpy(data)
- else:
- raise ValueError(
- 'modelscope error: The input type is restricted to audio address and nump array.'
- )
- output.append(data)
- return output
+ output = []
+ for i in range(len(inputs)):
+ if isinstance(inputs[i], str):
+ file_bytes = File.read(inputs[i])
+ data, fs = sf.load(io.BytesIO(file_bytes), dtype='float32')
+ if len(data.shape) == 2:
+ data = data[:, 0]
+ data = torch.from_numpy(data).unsqueeze(0)
+ data = data.squeeze(0)
+ elif isinstance(inputs[i], np.ndarray):
+ assert len(
+ inputs[i].shape
+ ) == 1, 'modelscope error: Input array should be [N, T]'
+ data = inputs[i]
+ if data.dtype in ['int16', 'int32', 'int64']:
+ data = (data / (1 << 15)).astype('float32')
+ else:
+ data = data.astype('float32')
+ data = torch.from_numpy(data)
+ else:
+ raise ValueError(
+ 'modelscope error: The input type is restricted to audio address and nump array.'
+ )
+ output.append(data)
+ return output
def sv_chunk(vad_segments: list, fs = 16000) -> list:
@@ -105,15 +101,19 @@
def extract_feature(audio):
features = []
+ feature_times = []
feature_lengths = []
for au in audio:
feature = Kaldi.fbank(
au.unsqueeze(0), num_mel_bins=80)
feature = feature - feature.mean(dim=0, keepdim=True)
- features.append(feature.unsqueeze(0))
- feature_lengths.append(au.shape[0])
- features = torch.cat(features)
- return features, feature_lengths
+ features.append(feature)
+ feature_times.append(au.shape[0])
+ feature_lengths.append(feature.shape[0])
+ # padding for batch inference
+ features_padded = pad_list(features, pad_value=0)
+ # features = torch.cat(features)
+ return features_padded, feature_lengths, feature_times
def postprocess(segments: list, vad_segments: list,
@@ -195,8 +195,8 @@
def distribute_spk(sentence_list, sd_time_list):
sd_sentence_list = []
for d in sentence_list:
- sentence_start = d['ts_list'][0][0]
- sentence_end = d['ts_list'][-1][1]
+ sentence_start = d['start']
+ sentence_end = d['end']
sentence_spk = 0
max_overlap = 0
for sd_time in sd_time_list:
@@ -211,8 +211,6 @@
d['spk'] = sentence_spk
sd_sentence_list.append(d)
return sd_sentence_list
-
-
class Storage(metaclass=ABCMeta):
diff --git a/funasr/models/ct_transformer/model.py b/funasr/models/ct_transformer/model.py
index e32aa25..fbf1804 100644
--- a/funasr/models/ct_transformer/model.py
+++ b/funasr/models/ct_transformer/model.py
@@ -239,6 +239,7 @@
cache_pop_trigger_limit = 200
results = []
meta_data = {}
+ punc_array = None
for mini_sentence_i in range(len(mini_sentences)):
mini_sentence = mini_sentences[mini_sentence_i]
mini_sentence_id = mini_sentences_id[mini_sentence_i]
@@ -320,8 +321,13 @@
elif new_mini_sentence[-1] != "." and new_mini_sentence[-1] != "?" and len(new_mini_sentence[-1].encode())==1:
new_mini_sentence_out = new_mini_sentence + "."
new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.sentence_end_id]
-
- result_i = {"key": key[0], "text": new_mini_sentence_out}
+ # keep a punctuations array for punc segment
+ if punc_array is None:
+ punc_array = punctuations
+ else:
+ punc_array = torch.cat([punc_array, punctuations], dim=0)
+
+ result_i = {"key": key[0], "text": new_mini_sentence_out, "punc_array": punc_array}
results.append(result_i)
return results, meta_data
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index 8186dff..63f179a 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -98,14 +98,14 @@
return res_txt, res
-def time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed):
+def timestamp_sentence(punc_id_list, timestamp_postprocessed, text_postprocessed):
punc_list = ['锛�', '銆�', '锛�', '銆�']
res = []
if text_postprocessed is None:
return res
- if time_stamp_postprocessed is None:
+ if timestamp_postprocessed is None:
return res
- if len(time_stamp_postprocessed) == 0:
+ if len(timestamp_postprocessed) == 0:
return res
if len(text_postprocessed) == 0:
return res
@@ -113,23 +113,22 @@
if punc_id_list is None or len(punc_id_list) == 0:
res.append({
'text': text_postprocessed.split(),
- "start": time_stamp_postprocessed[0][0],
- "end": time_stamp_postprocessed[-1][1],
- 'text_seg': text_postprocessed.split(),
- "ts_list": time_stamp_postprocessed,
+ "start": timestamp_postprocessed[0][0],
+ "end": timestamp_postprocessed[-1][1],
+ "timestamp": timestamp_postprocessed,
})
return res
- if len(punc_id_list) != len(time_stamp_postprocessed):
- print(" warning length mistach!!!!!!")
+ if len(punc_id_list) != len(timestamp_postprocessed):
+ logging.warning("length mismatch between punc and timestamp")
sentence_text = ""
sentence_text_seg = ""
ts_list = []
- sentence_start = time_stamp_postprocessed[0][0]
- sentence_end = time_stamp_postprocessed[0][1]
+ sentence_start = timestamp_postprocessed[0][0]
+ sentence_end = timestamp_postprocessed[0][1]
texts = text_postprocessed.split()
- punc_stamp_text_list = list(zip_longest(punc_id_list, time_stamp_postprocessed, texts, fillvalue=None))
+ punc_stamp_text_list = list(zip_longest(punc_id_list, timestamp_postprocessed, texts, fillvalue=None))
for punc_stamp_text in punc_stamp_text_list:
- punc_id, time_stamp, text = punc_stamp_text
+ punc_id, timestamp, text = punc_stamp_text
# sentence_text += text if text is not None else ''
if text is not None:
if 'a' <= text[0] <= 'z' or 'A' <= text[0] <= 'Z':
@@ -139,10 +138,10 @@
else:
sentence_text += text
sentence_text_seg += text + ' '
- ts_list.append(time_stamp)
+ ts_list.append(timestamp)
punc_id = int(punc_id) if punc_id is not None else 1
- sentence_end = time_stamp[1] if time_stamp is not None else sentence_end
+ sentence_end = timestamp[1] if timestamp is not None else sentence_end
if punc_id > 1:
sentence_text += punc_list[punc_id - 2]
@@ -150,8 +149,7 @@
'text': sentence_text,
"start": sentence_start,
"end": sentence_end,
- "text_seg": sentence_text_seg,
- "ts_list": ts_list
+ "timestamp": ts_list
})
sentence_text = ''
sentence_text_seg = ''
@@ -160,181 +158,4 @@
return res
-# class AverageShiftCalculator():
-# def __init__(self):
-# logging.warning("Calculating average shift.")
-# def __call__(self, file1, file2):
-# uttid_list1, ts_dict1 = self.read_timestamps(file1)
-# uttid_list2, ts_dict2 = self.read_timestamps(file2)
-# uttid_intersection = self._intersection(uttid_list1, uttid_list2)
-# res = self.as_cal(uttid_intersection, ts_dict1, ts_dict2)
-# logging.warning("Average shift of {} and {}: {}.".format(file1, file2, str(res)[:8]))
-# logging.warning("Following timestamp pair differs most: {}, detail:{}".format(self.max_shift, self.max_shift_uttid))
-#
-# def _intersection(self, list1, list2):
-# set1 = set(list1)
-# set2 = set(list2)
-# if set1 == set2:
-# logging.warning("Uttid same checked.")
-# return set1
-# itsc = list(set1 & set2)
-# logging.warning("Uttid differs: file1 {}, file2 {}, lines same {}.".format(len(list1), len(list2), len(itsc)))
-# return itsc
-#
-# def read_timestamps(self, file):
-# # read timestamps file in standard format
-# uttid_list = []
-# ts_dict = {}
-# with codecs.open(file, 'r') as fin:
-# for line in fin.readlines():
-# text = ''
-# ts_list = []
-# line = line.rstrip()
-# uttid = line.split()[0]
-# uttid_list.append(uttid)
-# body = " ".join(line.split()[1:])
-# for pd in body.split(';'):
-# if not len(pd): continue
-# # pdb.set_trace()
-# char, start, end = pd.lstrip(" ").split(' ')
-# text += char + ','
-# ts_list.append((float(start), float(end)))
-# # ts_lists.append(ts_list)
-# ts_dict[uttid] = (text[:-1], ts_list)
-# logging.warning("File {} read done.".format(file))
-# return uttid_list, ts_dict
-#
-# def _shift(self, filtered_timestamp_list1, filtered_timestamp_list2):
-# shift_time = 0
-# for fts1, fts2 in zip(filtered_timestamp_list1, filtered_timestamp_list2):
-# shift_time += abs(fts1[0] - fts2[0]) + abs(fts1[1] - fts2[1])
-# num_tokens = len(filtered_timestamp_list1)
-# return shift_time, num_tokens
-#
-# # def as_cal(self, uttid_list, ts_dict1, ts_dict2):
-# # # calculate average shift between timestamp1 and timestamp2
-# # # when characters differ, use edit distance alignment
-# # # and calculate the error between the same characters
-# # self._accumlated_shift = 0
-# # self._accumlated_tokens = 0
-# # self.max_shift = 0
-# # self.max_shift_uttid = None
-# # for uttid in uttid_list:
-# # (t1, ts1) = ts_dict1[uttid]
-# # (t2, ts2) = ts_dict2[uttid]
-# # _align, _align2, _align3 = [], [], []
-# # fts1, fts2 = [], []
-# # _t1, _t2 = [], []
-# # sm = edit_distance.SequenceMatcher(t1.split(','), t2.split(','))
-# # s = sm.get_opcodes()
-# # for j in range(len(s)):
-# # if s[j][0] == "replace" or s[j][0] == "insert":
-# # _align.append(0)
-# # if s[j][0] == "replace" or s[j][0] == "delete":
-# # _align3.append(0)
-# # elif s[j][0] == "equal":
-# # _align.append(1)
-# # _align3.append(1)
-# # else:
-# # continue
-# # # use s to index t2
-# # for a, ts , t in zip(_align, ts2, t2.split(',')):
-# # if a:
-# # fts2.append(ts)
-# # _t2.append(t)
-# # sm2 = edit_distance.SequenceMatcher(t2.split(','), t1.split(','))
-# # s = sm2.get_opcodes()
-# # for j in range(len(s)):
-# # if s[j][0] == "replace" or s[j][0] == "insert":
-# # _align2.append(0)
-# # elif s[j][0] == "equal":
-# # _align2.append(1)
-# # else:
-# # continue
-# # # use s2 tp index t1
-# # for a, ts, t in zip(_align3, ts1, t1.split(',')):
-# # if a:
-# # fts1.append(ts)
-# # _t1.append(t)
-# # if len(fts1) == len(fts2):
-# # shift_time, num_tokens = self._shift(fts1, fts2)
-# # self._accumlated_shift += shift_time
-# # self._accumlated_tokens += num_tokens
-# # if shift_time/num_tokens > self.max_shift:
-# # self.max_shift = shift_time/num_tokens
-# # self.max_shift_uttid = uttid
-# # else:
-# # logging.warning("length mismatch")
-# # return self._accumlated_shift / self._accumlated_tokens
-
-
-def convert_external_alphas(alphas_file, text_file, output_file):
- from funasr.models.paraformer.cif_predictor import cif_wo_hidden
- with open(alphas_file, 'r') as f1, open(text_file, 'r') as f2, open(output_file, 'w') as f3:
- for line1, line2 in zip(f1.readlines(), f2.readlines()):
- line1 = line1.rstrip()
- line2 = line2.rstrip()
- assert line1.split()[0] == line2.split()[0]
- uttid = line1.split()[0]
- alphas = [float(i) for i in line1.split()[1:]]
- new_alphas = np.array(remove_chunk_padding(alphas))
- new_alphas[-1] += 1e-4
- text = line2.split()[1:]
- if len(text) + 1 != int(new_alphas.sum()):
- # force resize
- new_alphas *= (len(text) + 1) / int(new_alphas.sum())
- peaks = cif_wo_hidden(torch.Tensor(new_alphas).unsqueeze(0), 1.0-1e-4)
- if " " in text:
- text = text.split()
- else:
- text = [i for i in text]
- res_str, _ = ts_prediction_lfr6_standard(new_alphas, peaks[0], text,
- force_time_shift=-7.0,
- sil_in_str=False)
- f3.write("{} {}\n".format(uttid, res_str))
-
-
-def remove_chunk_padding(alphas):
- # remove the padding part in alphas if using chunk paraformer for GPU
- START_ZERO = 45
- MID_ZERO = 75
- REAL_FRAMES = 360 # for chunk based encoder 10-120-10 and fsmn padding 5
- alphas = alphas[START_ZERO:] # remove the padding at beginning
- new_alphas = []
- while True:
- new_alphas = new_alphas + alphas[:REAL_FRAMES]
- alphas = alphas[REAL_FRAMES+MID_ZERO:]
- if len(alphas) < REAL_FRAMES: break
- return new_alphas
-
-SUPPORTED_MODES = ['cal_aas', 'read_ext_alphas']
-
-
-def main(args):
- # if args.mode == 'cal_aas':
- # asc = AverageShiftCalculator()
- # asc(args.input, args.input2)
- if args.mode == 'read_ext_alphas':
- convert_external_alphas(args.input, args.input2, args.output)
- else:
- logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES))
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='timestamp tools')
- parser.add_argument('--mode',
- default=None,
- type=str,
- choices=SUPPORTED_MODES,
- help='timestamp related toolbox')
- parser.add_argument('--input', default=None, type=str, help='input file path')
- parser.add_argument('--output', default=None, type=str, help='output file name')
- parser.add_argument('--input2', default=None, type=str, help='input2 file path')
- parser.add_argument('--kaldi-ts-type',
- default='v2',
- type=str,
- choices=['v0', 'v1', 'v2'],
- help='kaldi timestamp to write')
- args = parser.parse_args()
- main(args)
--
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