python/FunASR-XL.git

			@@ -107,6 +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()

			@@ -360,7 +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
			@@ -369,19 +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]
			)
			@@ -406,6 +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)