From fdafd3f6bc2f04d16e7cab5afcdb1257e87a8a78 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 17 十二月 2024 11:15:53 +0800
Subject: [PATCH] emotion2vec
---
funasr/auto/auto_model.py | 109 ++++++++++++++++++++++++++++++++++++++++--------------
1 files changed, 80 insertions(+), 29 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 01e6aaf..08308a2 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -14,13 +14,14 @@
import numpy as np
from tqdm import tqdm
+from omegaconf import DictConfig, ListConfig
from funasr.utils.misc import deep_update
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.utils.timestamp_tools import timestamp_sentence_en
-from funasr.download.download_from_hub import download_model
+from funasr.download.download_model_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.vad_utils import merge_vad
from funasr.utils.load_utils import load_audio_text_image_video
@@ -114,15 +115,12 @@
try:
from funasr.utils.version_checker import check_for_update
- check_for_update()
+ check_for_update(disable=kwargs.get("disable_update", False))
except:
pass
log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
logging.basicConfig(level=log_level)
-
- if not kwargs.get("disable_log", True):
- tables.print()
model, kwargs = self.build_model(**kwargs)
@@ -149,13 +147,14 @@
# if spk_model is not None, build spk model else None
spk_model = kwargs.get("spk_model", None)
spk_kwargs = {} if kwargs.get("spk_kwargs", {}) is None else kwargs.get("spk_kwargs", {})
+ cb_kwargs = {} if spk_kwargs.get("cb_kwargs", {}) is None else spk_kwargs.get("cb_kwargs", {})
if spk_model is not None:
logging.info("Building SPK model.")
spk_kwargs["model"] = spk_model
spk_kwargs["model_revision"] = kwargs.get("spk_model_revision", "master")
spk_kwargs["device"] = kwargs["device"]
spk_model, spk_kwargs = self.build_model(**spk_kwargs)
- self.cb_model = ClusterBackend().to(kwargs["device"])
+ self.cb_model = ClusterBackend(**cb_kwargs).to(kwargs["device"])
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.")
@@ -171,7 +170,8 @@
self.spk_kwargs = spk_kwargs
self.model_path = kwargs.get("model_path")
- def build_model(self, **kwargs):
+ @staticmethod
+ def build_model(**kwargs):
assert "model" in kwargs
if "model_conf" not in kwargs:
logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
@@ -189,21 +189,60 @@
# build tokenizer
tokenizer = kwargs.get("tokenizer", None)
- if tokenizer is not None:
- tokenizer_class = tables.tokenizer_classes.get(tokenizer)
- tokenizer = tokenizer_class(**kwargs.get("tokenizer_conf", {}))
- kwargs["token_list"] = (
- tokenizer.token_list if hasattr(tokenizer, "token_list") else None
- )
- kwargs["token_list"] = (
- tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else kwargs["token_list"]
- )
- vocab_size = len(kwargs["token_list"]) if kwargs["token_list"] is not None else -1
- if vocab_size == -1 and hasattr(tokenizer, "get_vocab_size"):
- vocab_size = tokenizer.get_vocab_size()
- else:
- vocab_size = -1
kwargs["tokenizer"] = tokenizer
+ kwargs["vocab_size"] = -1
+
+ if tokenizer is not None:
+ tokenizers = (
+ tokenizer.split(",") if isinstance(tokenizer, str) else tokenizer
+ ) # type of tokenizers is list!!!
+ tokenizers_conf = kwargs.get("tokenizer_conf", {})
+ tokenizers_build = []
+ vocab_sizes = []
+ token_lists = []
+
+ ### === only for kws ===
+ token_list_files = kwargs.get("token_lists", [])
+ seg_dicts = kwargs.get("seg_dicts", [])
+ ### === only for kws ===
+
+ if not isinstance(tokenizers_conf, (list, tuple, ListConfig)):
+ tokenizers_conf = [tokenizers_conf] * len(tokenizers)
+
+ for i, tokenizer in enumerate(tokenizers):
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer_conf = tokenizers_conf[i]
+
+ ### === only for kws ===
+ if len(token_list_files) > 1:
+ tokenizer_conf["token_list"] = token_list_files[i]
+ if len(seg_dicts) > 1:
+ tokenizer_conf["seg_dict"] = seg_dicts[i]
+ ### === only for kws ===
+
+ tokenizer = tokenizer_class(**tokenizer_conf)
+ tokenizers_build.append(tokenizer)
+ token_list = tokenizer.token_list if hasattr(tokenizer, "token_list") else None
+ token_list = (
+ tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else token_list
+ )
+ vocab_size = -1
+ if token_list is not None:
+ vocab_size = len(token_list)
+
+ if vocab_size == -1 and hasattr(tokenizer, "get_vocab_size"):
+ vocab_size = tokenizer.get_vocab_size()
+ token_lists.append(token_list)
+ vocab_sizes.append(vocab_size)
+
+ if len(tokenizers_build) <= 1:
+ tokenizers_build = tokenizers_build[0]
+ token_lists = token_lists[0]
+ vocab_sizes = vocab_sizes[0]
+
+ kwargs["tokenizer"] = tokenizers_build
+ kwargs["vocab_size"] = vocab_sizes
+ kwargs["token_list"] = token_lists
# build frontend
frontend = kwargs.get("frontend", None)
@@ -217,10 +256,11 @@
kwargs["frontend"] = frontend
# build model
model_class = tables.model_classes.get(kwargs["model"])
+ assert model_class is not None, f'{kwargs["model"]} is not registered'
model_conf = {}
deep_update(model_conf, kwargs.get("model_conf", {}))
deep_update(model_conf, kwargs)
- model = model_class(**model_conf, vocab_size=vocab_size)
+ model = model_class(**model_conf)
# init_param
init_param = kwargs.get("init_param", None)
@@ -244,6 +284,10 @@
elif kwargs.get("bf16", False):
model.to(torch.bfloat16)
model.to(device)
+
+ if not kwargs.get("disable_log", True):
+ tables.print()
+
return model, kwargs
def __call__(self, *args, **cfg):
@@ -261,6 +305,8 @@
def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
kwargs = self.kwargs if kwargs is None else kwargs
+ if "cache" in kwargs:
+ kwargs.pop("cache")
deep_update(kwargs, cfg)
model = self.model if model is None else model
model.eval()
@@ -312,7 +358,7 @@
speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
description = f"{speed_stats}, "
if pbar:
- pbar.update(1)
+ pbar.update(end_idx - beg_idx)
pbar.set_description(description)
time_speech_total += batch_data_time
time_escape_total += time_escape
@@ -334,9 +380,11 @@
end_vad = time.time()
# FIX(gcf): concat the vad clips for sense vocie model for better aed
- if kwargs.get("merge_vad", False):
+ if cfg.get("merge_vad", False):
for i in range(len(res)):
- res[i]["value"] = merge_vad(res[i]["value"], kwargs.get("merge_length", 15000))
+ res[i]["value"] = merge_vad(
+ res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
+ )
# step.2 compute asr model
model = self.model
@@ -376,6 +424,9 @@
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])
+
+ if kwargs["device"] == "cpu":
+ batch_size = 0
beg_idx = 0
beg_asr_total = time.time()
@@ -503,8 +554,8 @@
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
if self.spk_mode == "vad_segment": # recover sentence_list
sentence_list = []
- for res, vadsegment in zip(restored_data, vadsegments):
- if "timestamp" not in res:
+ for rest, vadsegment in zip(restored_data, vadsegments):
+ if "timestamp" not in rest:
logging.error(
"Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
@@ -514,8 +565,8 @@
{
"start": vadsegment[0],
"end": vadsegment[1],
- "sentence": res["text"],
- "timestamp": res["timestamp"],
+ "sentence": rest["text"],
+ "timestamp": rest["timestamp"],
}
)
elif self.spk_mode == "punc_segment":
--
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