From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/utils/speaker_utils.py | 108 +++++++++++++++++++++++++-----------------------------
1 files changed, 50 insertions(+), 58 deletions(-)
diff --git a/funasr/utils/speaker_utils.py b/funasr/utils/speaker_utils.py
index b769b85..e470849 100644
--- a/funasr/utils/speaker_utils.py
+++ b/funasr/utils/speaker_utils.py
@@ -19,58 +19,55 @@
audio_dur = 0
for i in range(len(audio)):
seg = audio[i]
- assert seg[1] >= seg[0], 'modelscope error: Wrong time stamps.'
- assert isinstance(seg[2], np.ndarray), 'modelscope error: Wrong data type.'
- assert int(seg[1] * 16000) - int(
- seg[0] * 16000
- ) == seg[2].shape[
- 0], 'modelscope error: audio data in list is inconsistent with time length.'
+ assert seg[1] >= seg[0], "modelscope error: Wrong time stamps."
+ assert isinstance(seg[2], np.ndarray), "modelscope error: Wrong data type."
+ assert (
+ int(seg[1] * 16000) - int(seg[0] * 16000) == seg[2].shape[0]
+ ), "modelscope error: audio data in list is inconsistent with time length."
if i > 0:
- assert seg[0] >= audio[
- i - 1][1], 'modelscope error: Wrong time stamps.'
+ assert seg[0] >= audio[i - 1][1], "modelscope error: Wrong time stamps."
audio_dur += seg[1] - seg[0]
return audio_dur
# assert audio_dur > 5, 'modelscope error: The effective audio duration is too short.'
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)
+ 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:
- raise ValueError(
- 'modelscope error: The input type is restricted to audio address and nump array.'
- )
- output.append(data)
- return output
+ 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:
+def sv_chunk(vad_segments: list, fs=16000) -> list:
config = {
- 'seg_dur': 1.5,
- 'seg_shift': 0.75,
- }
+ "seg_dur": 1.5,
+ "seg_shift": 0.75,
+ }
+
def seg_chunk(seg_data):
seg_st = seg_data[0]
data = seg_data[2]
- chunk_len = int(config['seg_dur'] * fs)
- chunk_shift = int(config['seg_shift'] * fs)
+ chunk_len = int(config["seg_dur"] * fs)
+ chunk_shift = int(config["seg_shift"] * fs)
last_chunk_ed = 0
seg_res = []
for chunk_st in range(0, data.shape[0], chunk_shift):
@@ -81,13 +78,8 @@
chunk_st = max(0, chunk_ed - chunk_len)
chunk_data = data[chunk_st:chunk_ed]
if chunk_data.shape[0] < chunk_len:
- chunk_data = np.pad(chunk_data,
- (0, chunk_len - chunk_data.shape[0]),
- 'constant')
- seg_res.append([
- chunk_st / fs + seg_st, chunk_ed / fs + seg_st,
- chunk_data
- ])
+ chunk_data = np.pad(chunk_data, (0, chunk_len - chunk_data.shape[0]), "constant")
+ seg_res.append([chunk_st / fs + seg_st, chunk_ed / fs + seg_st, chunk_data])
return seg_res
segs = []
@@ -100,16 +92,16 @@
def extract_feature(audio):
features = []
for au in audio:
- feature = Kaldi.fbank(
- au.unsqueeze(0), num_mel_bins=80)
+ feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80)
feature = feature - feature.mean(dim=0, keepdim=True)
features.append(feature.unsqueeze(0))
features = torch.cat(features)
return features
-def postprocess(segments: list, vad_segments: list,
- labels: np.ndarray, embeddings: np.ndarray) -> list:
+def postprocess(
+ segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray
+) -> list:
assert len(segments) == len(labels)
labels = correct_labels(labels)
distribute_res = []
@@ -154,15 +146,16 @@
new_labels.append(id2id[i])
return np.array(new_labels)
+
def merge_seque(distribute_res):
res = [distribute_res[0]]
for i in range(1, len(distribute_res)):
- if distribute_res[i][2] != res[-1][2] or distribute_res[i][
- 0] > res[-1][1]:
+ if distribute_res[i][2] != res[-1][2] or distribute_res[i][0] > res[-1][1]:
res.append(distribute_res[i])
else:
res[-1][1] = distribute_res[i][1]
return res
+
def smooth(res, mindur=1):
# short segments are assigned to nearest speakers.
@@ -187,19 +180,18 @@
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["ts_list"][0][0]
+ sentence_end = d["ts_list"][-1][1]
sentence_spk = 0
max_overlap = 0
for sd_time in sd_time_list:
spk_st, spk_ed, spk = sd_time
- spk_st = spk_st*1000
- spk_ed = spk_ed*1000
- overlap = max(
- min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0)
+ spk_st = spk_st * 1000
+ spk_ed = spk_ed * 1000
+ overlap = max(min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0)
if overlap > max_overlap:
max_overlap = overlap
sentence_spk = spk
- d['spk'] = sentence_spk
+ d["spk"] = sentence_spk
sd_sentence_list.append(d)
return sd_sentence_list
--
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