From 4adb76a6edbca93aae7caa83382e764d7b058f07 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 08 五月 2024 19:21:58 +0800
Subject: [PATCH] Dev gzf exp (#1707)
---
funasr/auto/auto_model.py | 49 ++++++++++++++++++++++++++++---------------------
1 files changed, 28 insertions(+), 21 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 32fd560..97eb325 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -26,6 +26,7 @@
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
from funasr.utils import export_utils
+from funasr.utils import misc
try:
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
@@ -35,14 +36,7 @@
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
- """
-
- :param input:
- :param input_len:
- :param data_type:
- :param frontend:
- :return:
- """
+ """ """
data_list = []
key_list = []
filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]
@@ -73,7 +67,8 @@
key_list.append(key)
else:
if key is None:
- key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ # key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ key = misc.extract_filename_without_extension(data_in)
data_list = [data_in]
key_list = [key]
elif isinstance(data_in, (list, tuple)):
@@ -90,10 +85,14 @@
else:
# [audio sample point, fbank, text]
data_list = data_in
- key_list = [
- "rand_key_" + "".join(random.choice(chars) for _ in range(13))
- for _ in range(len(data_in))
- ]
+ key_list = []
+ for data_i in data_in:
+ if isinstance(data_i, str) and os.path.exists(data_i):
+ key = misc.extract_filename_without_extension(data_i)
+ else:
+ key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ key_list.append(key)
+
else: # raw text; audio sample point, fbank; bytes
if isinstance(data_in, bytes): # audio bytes
data_in = load_bytes(data_in)
@@ -108,6 +107,10 @@
class AutoModel:
def __init__(self, **kwargs):
+
+ log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+ logging.basicConfig(level=log_level)
+
if not kwargs.get("disable_log", True):
tables.print()
@@ -361,7 +364,6 @@
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])
- batch_size_ms_cum = 0
beg_idx = 0
beg_asr_total = time.time()
time_speech_total_per_sample = speech_lengths / 16000
@@ -370,19 +372,22 @@
# pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)
all_segments = []
+ max_len_in_batch = 0
+ end_idx = 1
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]
+ sample_length = sorted_data[j][0][1] - sorted_data[j][0][0]
+ potential_batch_length = max(max_len_in_batch, sample_length) * (j + 1 - beg_idx)
+ # 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 (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0])
- < batch_size_threshold_ms
+ and sample_length < batch_size_threshold_ms
+ and potential_batch_length < batch_size
):
+ max_len_in_batch = max(max_len_in_batch, sample_length)
+ end_idx += 1
continue
- 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]
)
@@ -407,6 +412,8 @@
)
results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"]
beg_idx = end_idx
+ end_idx += 1
+ max_len_in_batch = sample_length
if len(results) < 1:
continue
results_sorted.extend(results)
--
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