From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/auto/auto_model.py | 136 +++++++++++++++++++++++++--------------------
1 files changed, 76 insertions(+), 60 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index ffb56a5..e95cfd8 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -6,6 +6,7 @@
import string
import logging
import os.path
+import numpy as np
from tqdm import tqdm
from omegaconf import DictConfig, OmegaConf, ListConfig
@@ -19,7 +20,10 @@
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
+try:
+ from funasr.models.campplus.cluster_backend import ClusterBackend
+except:
+ print("If you want to use the speaker diarization, please `pip install hdbscan`")
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
@@ -87,7 +91,8 @@
class AutoModel:
def __init__(self, **kwargs):
- tables.print()
+ if not kwargs.get("disable_log", False):
+ tables.print()
model, kwargs = self.build_model(**kwargs)
@@ -96,7 +101,7 @@
vad_kwargs = kwargs.get("vad_model_revision", None)
if vad_model is not None:
logging.info("Building VAD model.")
- vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+ vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs, "device": kwargs["device"]}
vad_model, vad_kwargs = self.build_model(**vad_kwargs)
# if punc_model is not None, build punc model else None
@@ -104,7 +109,7 @@
punc_kwargs = kwargs.get("punc_model_revision", None)
if punc_model is not None:
logging.info("Building punc model.")
- punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+ punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs, "device": kwargs["device"]}
punc_model, punc_kwargs = self.build_model(**punc_kwargs)
# if spk_model is not None, build spk model else None
@@ -112,17 +117,13 @@
spk_kwargs = kwargs.get("spk_model_revision", None)
if spk_model is not None:
logging.info("Building SPK model.")
- spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
+ spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs, "device": kwargs["device"]}
spk_model, spk_kwargs = self.build_model(**spk_kwargs)
- self.cb_model = ClusterBackend()
+ self.cb_model = ClusterBackend().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.")
self.spk_mode = spk_mode
- self.preset_spk_num = kwargs.get("preset_spk_num", None)
- if self.preset_spk_num:
- logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
- logging.warning("Many to print when using speaker model...")
self.kwargs = kwargs
self.model = model
@@ -132,8 +133,7 @@
self.punc_kwargs = punc_kwargs
self.spk_model = spk_model
self.spk_kwargs = spk_kwargs
- self.model_path = kwargs["model_path"]
-
+ self.model_path = kwargs.get("model_path")
def build_model(self, **kwargs):
assert "model" in kwargs
@@ -144,9 +144,9 @@
set_all_random_seed(kwargs.get("seed", 0))
device = kwargs.get("device", "cuda")
- if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
device = "cpu"
- # kwargs["batch_size"] = 1
+ kwargs["batch_size"] = 1
kwargs["device"] = device
if kwargs.get("ncpu", None):
@@ -174,7 +174,7 @@
# build model
model_class = tables.model_classes.get(kwargs["model"])
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
- model.eval()
+
model.to(device)
# init_param
@@ -198,8 +198,6 @@
res = self.model(*args, kwargs)
return res
-
-
def generate(self, input, input_len=None, **cfg):
if self.vad_model is None:
return self.inference(input, input_len=input_len, **cfg)
@@ -211,6 +209,7 @@
kwargs = self.kwargs if kwargs is None else kwargs
kwargs.update(cfg)
model = self.model if model is None else model
+ model.eval()
batch_size = kwargs.get("batch_size", 1)
# if kwargs.get("device", "cpu") == "cpu":
@@ -221,7 +220,8 @@
speed_stats = {}
asr_result_list = []
num_samples = len(data_list)
- pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
+ disable_pbar = kwargs.get("disable_pbar", False)
+ pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
time_speech_total = 0.0
time_escape_total = 0.0
for beg_idx in range(0, num_samples, batch_size):
@@ -229,7 +229,7 @@
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
+ if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
batch["data_in"] = data_batch[0]
batch["data_lengths"] = input_len
@@ -239,8 +239,7 @@
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
@@ -252,12 +251,15 @@
description = (
f"{speed_stats}, "
)
- pbar.set_description(description)
+ if pbar:
+ pbar.update(1)
+ 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}")
+
+ if pbar:
+ # 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
@@ -281,10 +283,10 @@
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
+ time_speech_total_all_samples = 1e-6
beg_total = time.time()
- pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+ pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
for i in range(len(res)):
key = res[i]["key"]
vadsegments = res[i]["value"]
@@ -309,7 +311,11 @@
time_speech_total_per_sample = speech_lengths/16000
time_speech_total_all_samples += time_speech_total_per_sample
+ # pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)
+
+ all_segments = []
for j, _ in enumerate(range(0, n)):
+ # pbar_sample.update(1)
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 (
@@ -318,32 +324,30 @@
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.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+ results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **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]]]
+ vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
+ sorted_data[beg_idx:end_idx][_b][0][1]/1000.0,
+ np.array(speech_j[_b])]]
segments = sv_chunk(vad_segments)
all_segments.extend(segments)
speech_b = [i[2] for i in segments]
- spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+ spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, disable_pbar=True, **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}")
-
+
+ # end_asr_total = time.time()
+ # time_escape_total_per_sample = end_asr_total - beg_asr_total
+ # pbar_sample.update(1)
+ # pbar_sample.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]
@@ -366,7 +370,7 @@
result[k] = restored_data[j][k]
else:
result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
- elif k == 'text':
+ elif 'text' in k:
if k not in result:
result[k] = restored_data[j][k]
else:
@@ -380,39 +384,51 @@
# step.3 compute punc model
if self.punc_model is not None:
self.punc_kwargs.update(cfg)
- punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
- result["text_with_punc"] = punc_res[0]["text"]
-
+ punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
+ import copy; raw_text = copy.copy(result["text"])
+ result["text"] = punc_res[0]["text"]
+
# speaker embedding cluster after resorted
- if self.spk_model is not None:
+ if self.spk_model is not None and kwargs.get('return_spk_res', True):
all_segments = sorted(all_segments, key=lambda x: x[0])
spk_embedding = result['spk_embedding']
- labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
- del result['spk_embedding']
+ labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs.get('preset_spk_num', None))
+ # del result['spk_embedding']
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
- if self.spk_mode == 'vad_segment':
+ if self.spk_mode == 'vad_segment': # recover sentence_list
sentence_list = []
for res, vadsegment in zip(restored_data, vadsegments):
sentence_list.append({"start": vadsegment[0],\
"end": vadsegment[1],
- "sentence": res['text'],
+ "sentence": res['raw_text'],
"timestamp": res['timestamp']})
- else: # punc_segment
+ elif self.spk_mode == 'punc_segment':
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
result['timestamp'], \
- result['text'])
+ result['raw_text'])
distribute_spk(sentence_list, sv_output)
result['sentence_info'] = sentence_list
+ elif kwargs.get("sentence_timestamp", False):
+ sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+ result['timestamp'], \
+ result['raw_text'])
+ result['sentence_info'] = sentence_list
+ del result['spk_embedding']
result["key"] = key
results_ret_list.append(result)
+ end_asr_total = time.time()
+ time_escape_total_per_sample = end_asr_total - beg_asr_total
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}")
+ pbar_total.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ f"time_speech: {time_speech_total_per_sample: 0.3f}, "
+ f"time_escape: {time_escape_total_per_sample:0.3f}")
+
+
+ # end_total = time.time()
+ # time_escape_total_all_samples = end_total - beg_total
+ # print(f"rtf_avg_all: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+ # f"time_speech_all: {time_speech_total_all_samples: 0.3f}, "
+ # f"time_escape_all: {time_escape_total_all_samples:0.3f}")
return results_ret_list
--
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