From 73613cefc97bd43699d10b8d162c69b2c4544ad5 Mon Sep 17 00:00:00 2001
From: 夜雨飘零 <yeyupiaoling@foxmail.com>
Date: 星期一, 04 十二月 2023 21:41:07 +0800
Subject: [PATCH] 增加分角色语音识别对ERes2Net模型的支持。
---
funasr/bin/asr_inference_launch.py | 75 +++++++++++++++++++++++--------------
1 files changed, 47 insertions(+), 28 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index e1a32c5..402a911 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -20,7 +20,8 @@
import numpy as np
import torch
import torchaudio
-import soundfile
+# import librosa
+import librosa
import yaml
from funasr.bin.asr_infer import Speech2Text
@@ -47,13 +48,13 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils.vad_utils import slice_padding_fbank
-from funasr.utils.speaker_utils import (check_audio_list,
- sv_preprocess,
- sv_chunk,
- CAMPPlus,
- extract_feature,
+from funasr.utils.speaker_utils import (check_audio_list,
+ sv_preprocess,
+ sv_chunk,
+ CAMPPlus,
+ extract_feature,
postprocess,
- distribute_spk)
+ distribute_spk, ERes2Net)
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.utils.cluster_backend import ClusterBackend
from funasr.utils.modelscope_utils import get_cache_dir
@@ -818,6 +819,10 @@
)
sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
+ if not os.path.exists(sv_model_file):
+ sv_model_file = asr_model_file.replace("model.pb", "pretrained_eres2net_aug.ckpt")
+ if not os.path.exists(sv_model_file):
+ raise FileNotFoundError("sv_model_file not found: {}".format(sv_model_file))
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
@@ -943,8 +948,14 @@
##### speaker_verification #####
##################################
# load sv model
- sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
- sv_model = CAMPPlus()
+ sv_model_dict = torch.load(sv_model_file)
+ print(f'load sv model params: {sv_model_file}')
+ if os.path.basename(sv_model_file) == "campplus_cn_common.bin":
+ sv_model = CAMPPlus()
+ else:
+ sv_model = ERes2Net()
+ if ngpu > 0:
+ sv_model.cuda()
sv_model.load_state_dict(sv_model_dict)
sv_model.eval()
cb_model = ClusterBackend()
@@ -955,24 +966,31 @@
ed = int(vadsegment[1]) / 1000
vad_segments.append(
[st, ed, audio[int(st * 16000):int(ed * 16000)]])
- check_audio_list(vad_segments)
- # sv pipeline
- segments = sv_chunk(vad_segments)
- embeddings = []
- for s in segments:
- #_, embs = self.sv_pipeline([s[2]], output_emb=True)
- # embeddings.append(embs)
- wavs = sv_preprocess([s[2]])
- # embs = self.forward(wavs)
- embs = []
- for x in wavs:
- x = extract_feature([x])
- embs.append(sv_model(x))
- embs = torch.cat(embs)
- embeddings.append(embs.detach().numpy())
- embeddings = np.concatenate(embeddings)
- labels = cb_model(embeddings)
- sv_output = postprocess(segments, vad_segments, labels, embeddings)
+ audio_dur = check_audio_list(vad_segments)
+ if audio_dur > 5:
+ # sv pipeline
+ segments = sv_chunk(vad_segments)
+ embeddings = []
+ for s in segments:
+ #_, embs = self.sv_pipeline([s[2]], output_emb=True)
+ # embeddings.append(embs)
+ wavs = sv_preprocess([s[2]])
+ # embs = self.forward(wavs)
+ embs = []
+ for x in wavs:
+ x = extract_feature([x])
+ if ngpu > 0:
+ x = x.cuda()
+ embs.append(sv_model(x))
+ embs = torch.cat(embs)
+ embeddings.append(embs.cpu().detach().numpy())
+ embeddings = np.concatenate(embeddings)
+ labels = cb_model(embeddings)
+ sv_output = postprocess(segments, vad_segments, labels, embeddings)
+ else:
+ # fake speaker res for too shot utterance
+ sv_output = [[0.0, vadsegments[-1][-1]/1000.0, 0]]
+ logging.warning("Too short utterence found: {}, return default speaker results.".format(keys))
speech, speech_lengths = batch["speech"], batch["speech_lengths"]
@@ -1281,7 +1299,8 @@
try:
raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
except:
- raw_inputs = soundfile.read(data_path_and_name_and_type[0], dtype='float32')[0]
+ # raw_inputs = librosa.load(data_path_and_name_and_type[0], dtype='float32')[0]
+ raw_inputs, sr = librosa.load(data_path_and_name_and_type[0], dtype='float32')
if raw_inputs.ndim == 2:
raw_inputs = raw_inputs[:, 0]
raw_inputs = torch.tensor(raw_inputs)
--
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