From 4f078d1cbd4dfd1ffce31a563cc792098174f920 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 25 一月 2024 15:05:12 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/auto/auto_model.py | 26 ++++++++++++++------------
1 files changed, 14 insertions(+), 12 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 0538f66..5bfc080 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
@@ -96,7 +97,7 @@
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 +105,7 @@
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
@@ -112,9 +113,9 @@
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.")
@@ -145,7 +146,7 @@
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", 0) == 0:
device = "cpu"
kwargs["batch_size"] = 1
kwargs["device"] = device
@@ -223,7 +224,7 @@
asr_result_list = []
num_samples = len(data_list)
disable_pbar = kwargs.get("disable_pbar", False)
- pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None
+ 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):
@@ -334,7 +335,7 @@
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]]]
+ np.array(speech_j[_b])]]
segments = sv_chunk(vad_segments)
all_segments.extend(segments)
speech_b = [i[2] for i in segments]
@@ -349,6 +350,7 @@
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}")
@@ -376,7 +378,7 @@
result[k] = restored_data[j][k]
else:
result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
- elif k == 'text':
+ elif k == 'raw_text':
if k not in result:
result[k] = restored_data[j][k]
else:
@@ -391,13 +393,13 @@
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"]
+ result["text"] = 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, oracle_num=self.preset_spk_num)
+ labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
del result['spk_embedding']
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
if self.spk_mode == 'vad_segment':
@@ -405,12 +407,12 @@
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
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
--
Gitblit v1.9.1