python/FunASR-XL.git

			@@ -6,6 +6,7 @@
			import string
			import logging
			import os.path
			import numpy as np
			from tqdm import tqdm
			from omegaconf import DictConfig, OmegaConf, ListConfig

			@@ -19,7 +20,10 @@
			from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
			from funasr.utils.timestamp_tools import timestamp_sentence
			from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
			from funasr.models.campplus.cluster_backend import ClusterBackend
			try:
			from funasr.models.campplus.cluster_backend import ClusterBackend
			except:
			print("If you want to use the speaker diarization, please `pip install hdbscan`")


			def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
			@@ -87,7 +91,8 @@
			class AutoModel:

			def __init__(self, **kwargs):
			tables.print()
			if not kwargs.get("disable_log", False):
			tables.print()

			model, kwargs = self.build_model(**kwargs)

			@@ -96,7 +101,7 @@
			vad_kwargs = kwargs.get("vad_model_revision", None)
			if vad_model is not None:
			logging.info("Building VAD model.")
			vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
			vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs, "device": kwargs["device"]}
			vad_model, vad_kwargs = self.build_model(**vad_kwargs)

			# if punc_model is not None, build punc model else None
			@@ -104,7 +109,7 @@
			punc_kwargs = kwargs.get("punc_model_revision", None)
			if punc_model is not None:
			logging.info("Building punc model.")
			punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
			punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs, "device": kwargs["device"]}
			punc_model, punc_kwargs = self.build_model(**punc_kwargs)

			# if spk_model is not None, build spk model else None
			@@ -112,17 +117,13 @@
			spk_kwargs = kwargs.get("spk_model_revision", None)
			if spk_model is not None:
			logging.info("Building SPK model.")
			spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
			spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs, "device": kwargs["device"]}
			spk_model, spk_kwargs = self.build_model(**spk_kwargs)
			self.cb_model = ClusterBackend()
			self.cb_model = ClusterBackend().to(kwargs["device"])
			spk_mode = kwargs.get("spk_mode", 'punc_segment')
			if spk_mode not in ["default", "vad_segment", "punc_segment"]:
			logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
			self.spk_mode = spk_mode
			self.preset_spk_num = kwargs.get("preset_spk_num", None)
			if self.preset_spk_num:
			logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
			logging.warning("Many to print when using speaker model...")

			self.kwargs = kwargs
			self.model = model
			@@ -132,8 +133,7 @@
			self.punc_kwargs = punc_kwargs
			self.spk_model = spk_model
			self.spk_kwargs = spk_kwargs
			self.model_path = kwargs["model_path"]

			self.model_path = kwargs.get("model_path")

			def build_model(self, **kwargs):
			assert "model" in kwargs
			@@ -144,9 +144,9 @@
			set_all_random_seed(kwargs.get("seed", 0))

			device = kwargs.get("device", "cuda")
			if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
			if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
			device = "cpu"
			# kwargs["batch_size"] = 1
			kwargs["batch_size"] = 1
			kwargs["device"] = device

			if kwargs.get("ncpu", None):
			@@ -174,7 +174,7 @@
			# build model
			model_class = tables.model_classes.get(kwargs["model"])
			model = model_class(kwargs, kwargs["model_conf"], vocab_size=vocab_size)
			model.eval()

			model.to(device)

			# init_param
			@@ -198,8 +198,6 @@
			res = self.model(*args, kwargs)
			return res



			def generate(self, input, input_len=None, **cfg):
			if self.vad_model is None:
			return self.inference(input, input_len=input_len, **cfg)
			@@ -211,6 +209,7 @@
			kwargs = self.kwargs if kwargs is None else kwargs
			kwargs.update(cfg)
			model = self.model if model is None else model
			model.eval()

			batch_size = kwargs.get("batch_size", 1)
			# if kwargs.get("device", "cpu") == "cpu":
			@@ -221,7 +220,8 @@
			speed_stats = {}
			asr_result_list = []
			num_samples = len(data_list)
			pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
			disable_pbar = kwargs.get("disable_pbar", False)
			pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
			time_speech_total = 0.0
			time_escape_total = 0.0
			for beg_idx in range(0, num_samples, batch_size):
			@@ -229,7 +229,7 @@
			data_batch = data_list[beg_idx:end_idx]
			key_batch = key_list[beg_idx:end_idx]
			batch = {"data_in": data_batch, "key": key_batch}
			if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
			if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
			batch["data_in"] = data_batch[0]
			batch["data_lengths"] = input_len

			@@ -239,8 +239,7 @@
			time2 = time.perf_counter()

			asr_result_list.extend(results)
			pbar.update(1)


			# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
			batch_data_time = meta_data.get("batch_data_time", -1)
			time_escape = time2 - time1
			@@ -252,12 +251,15 @@
			description = (
			f"{speed_stats}, "
			)
			pbar.set_description(description)
			if pbar:
			pbar.update(1)
			pbar.set_description(description)
			time_speech_total += batch_data_time
			time_escape_total += time_escape

			pbar.update(1)
			pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")

			if pbar:
			# pbar.update(1)
			pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
			torch.cuda.empty_cache()
			return asr_result_list

			@@ -281,10 +283,10 @@

			key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
			results_ret_list = []
			time_speech_total_all_samples = 0.0
			time_speech_total_all_samples = 1e-6

			beg_total = time.time()
			pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
			pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
			for i in range(len(res)):
			key = res[i]["key"]
			vadsegments = res[i]["value"]
			@@ -309,7 +311,11 @@
			time_speech_total_per_sample = speech_lengths/16000
			time_speech_total_all_samples += time_speech_total_per_sample

			# pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)

			all_segments = []
			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])
			if j < n - 1 and (
			batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
			@@ -318,32 +324,30 @@
			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])
			results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
			results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
			if self.spk_model is not None:
			all_segments = []
			# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
			for _b in range(len(speech_j)):
			vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
			sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
			speech_j[_b]]]
			vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
			sorted_data[beg_idx:end_idx][_b][0][1]/1000.0,
			np.array(speech_j[_b])]]
			segments = sv_chunk(vad_segments)
			all_segments.extend(segments)
			speech_b = [i[2] for i in segments]
			spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
			spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, disable_pbar=True, **cfg)
			results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
			beg_idx = end_idx
			if len(results) < 1:
			continue
			results_sorted.extend(results)


			pbar_total.update(1)
			end_asr_total = time.time()
			time_escape_total_per_sample = end_asr_total - beg_asr_total
			pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
			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}")


			# end_asr_total = time.time()
			# time_escape_total_per_sample = end_asr_total - beg_asr_total
			# pbar_sample.update(1)
			# pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
			# 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}")

			restored_data = [0] * n
			for j in range(n):
			index = sorted_data[j][1]
			@@ -366,7 +370,7 @@
			result[k] = restored_data[j][k]
			else:
			result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
			elif k == 'text':
			elif 'text' in k:
			if k not in result:
			result[k] = restored_data[j][k]
			else:
			@@ -380,39 +384,51 @@
			# step.3 compute punc model
			if self.punc_model is not None:
			self.punc_kwargs.update(cfg)
			punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
			result["text_with_punc"] = punc_res[0]["text"]

			punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
			import copy; raw_text = copy.copy(result["text"])
			result["text"] = punc_res[0]["text"]

			# speaker embedding cluster after resorted
			if self.spk_model is not None:
			if self.spk_model is not None and kwargs.get('return_spk_res', True):
			all_segments = sorted(all_segments, key=lambda x: x[0])
			spk_embedding = result['spk_embedding']
			labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
			del result['spk_embedding']
			labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs.get('preset_spk_num', None))
			# del result['spk_embedding']
			sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
			if self.spk_mode == 'vad_segment':
			if self.spk_mode == 'vad_segment': # recover sentence_list
			sentence_list = []
			for res, vadsegment in zip(restored_data, vadsegments):
			sentence_list.append({"start": vadsegment[0],\
			"end": vadsegment[1],
			"sentence": res['text'],
			"sentence": res['raw_text'],
			"timestamp": res['timestamp']})
			else: # punc_segment
			elif self.spk_mode == 'punc_segment':
			sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
			result['timestamp'], \
			result['text'])
			result['raw_text'])
			distribute_spk(sentence_list, sv_output)
			result['sentence_info'] = sentence_list
			elif kwargs.get("sentence_timestamp", False):
			sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
			result['timestamp'], \
			result['raw_text'])
			result['sentence_info'] = sentence_list
			del result['spk_embedding']

			result["key"] = key
			results_ret_list.append(result)
			end_asr_total = time.time()
			time_escape_total_per_sample = end_asr_total - beg_asr_total
			pbar_total.update(1)

			pbar_total.update(1)
			end_total = time.time()
			time_escape_total_all_samples = end_total - beg_total
			pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
			f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
			f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
			pbar_total.set_description(f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
			f"time_speech: {time_speech_total_per_sample: 0.3f}, "
			f"time_escape: {time_escape_total_per_sample:0.3f}")


			# end_total = time.time()
			# time_escape_total_all_samples = end_total - beg_total
			# print(f"rtf_avg_all: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
			# f"time_speech_all: {time_speech_total_all_samples: 0.3f}, "
			# f"time_escape_all: {time_escape_total_all_samples:0.3f}")
			return results_ret_list