From d43f77408b8f3e169c59dfb6b6d82e45e6b91714 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 11 六月 2024 19:19:06 +0800
Subject: [PATCH] decoding
---
funasr/auto/auto_model.py | 36 ++++++++++++++++++++++++------------
1 files changed, 24 insertions(+), 12 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 577c328..bbaf657 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -42,8 +42,9 @@
filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]
chars = string.ascii_letters + string.digits
- if isinstance(data_in, str) and data_in.startswith("http"): # url
- data_in = download_from_url(data_in)
+ if isinstance(data_in, str):
+ if data_in.startswith("http://") or data_in.startswith("https://"): # url
+ data_in = download_from_url(data_in)
if isinstance(data_in, str) and os.path.exists(
data_in
@@ -211,7 +212,6 @@
deep_update(model_conf, kwargs.get("model_conf", {}))
deep_update(model_conf, kwargs)
model = model_class(**model_conf, vocab_size=vocab_size)
- model.to(device)
# init_param
init_param = kwargs.get("init_param", None)
@@ -232,6 +232,9 @@
# fp16
if kwargs.get("fp16", False):
model.to(torch.float16)
+ elif kwargs.get("bf16", False):
+ model.to(torch.bfloat16)
+ model.to(device)
return model, kwargs
def __call__(self, *args, **cfg):
@@ -284,7 +287,7 @@
with torch.no_grad():
res = model.inference(**batch, **kwargs)
if isinstance(res, (list, tuple)):
- results = res[0]
+ results = res[0] if len(res) > 0 else [{"text": ""}]
meta_data = res[1] if len(res) > 1 else {}
time2 = time.perf_counter()
@@ -358,13 +361,13 @@
results_sorted = []
if not len(sorted_data):
+ results_ret_list.append({"key": key, "text": "", "timestamp": []})
logging.info("decoding, utt: {}, empty speech".format(key))
continue
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
@@ -373,19 +376,22 @@
# 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]
)
@@ -410,6 +416,8 @@
)
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)
@@ -421,6 +429,10 @@
# 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}")
+ if len(results_sorted) != n:
+ results_ret_list.append({"key": key, "text": "", "timestamp": []})
+ logging.info("decoding, utt: {}, empty result".format(key))
+ continue
restored_data = [0] * n
for j in range(n):
index = sorted_data[j][1]
--
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