python/FunASR-XL.git

			@@ -1,5 +1,6 @@
			import argparse
			import logging
			from optparse import Option
			import sys
			import json
			from pathlib import Path
			@@ -11,15 +12,12 @@
			from typing import Union
			from typing import Dict

			import math
			import numpy as np
			import torch
			from typeguard import check_argument_types
			from typeguard import check_return_type

			from funasr.fileio.datadir_writer import DatadirWriter
			from funasr.modules.scorers.scorer_interface import BatchScorerInterface
			from funasr.modules.subsampling import TooShortUttError
			from funasr.datasets.preprocessor import LMPreprocessor
			from funasr.tasks.asr import ASRTaskAligner as ASRTask
			from funasr.torch_utils.device_funcs import to_device
			from funasr.torch_utils.set_all_random_seed import set_all_random_seed
			@@ -28,9 +26,10 @@
			from funasr.utils.types import str2bool
			from funasr.utils.types import str2triple_str
			from funasr.utils.types import str_or_none
			from funasr.utils import asr_utils, wav_utils, postprocess_utils
			from funasr.models.frontend.wav_frontend import WavFrontend
			from funasr.text.token_id_converter import TokenIDConverter
			from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard


			header_colors = '\033[95m'
			end_colors = '\033[0m'
			@@ -40,61 +39,6 @@
			'audio_fs': 16000,
			'model_fs': 16000
			}

			def time_stamp_lfr6_advance(us_alphas, us_cif_peak, char_list):
			START_END_THRESHOLD = 5
			MAX_TOKEN_DURATION = 12
			TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
			if len(us_cif_peak.shape) == 2:
			alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
			else:
			alphas, cif_peak = us_alphas, us_cif_peak
			num_frames = cif_peak.shape[0]
			if char_list[-1] == '</s>':
			char_list = char_list[:-1]
			# char_list = [i for i in text]
			timestamp_list = []
			new_char_list = []
			# for bicif model trained with large data, cif2 actually fires when a character starts
			# so treat the frames between two peaks as the duration of the former token
			fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 3.2 # total offset
			num_peak = len(fire_place)
			assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
			# begin silence
			if fire_place[0] > START_END_THRESHOLD:
			# char_list.insert(0, '<sil>')
			timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
			new_char_list.append('<sil>')
			# tokens timestamp
			for i in range(len(fire_place)-1):
			new_char_list.append(char_list[i])
			if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
			timestamp_list.append([fire_place[i]TIME_RATE, fire_place[i+1]TIME_RATE])
			else:
			# cut the duration to token and sil of the 0-weight frames last long
			_split = fire_place[i] + MAX_TOKEN_DURATION
			timestamp_list.append([fire_place[i]TIME_RATE, _splitTIME_RATE])
			timestamp_list.append([_splitTIME_RATE, fire_place[i+1]TIME_RATE])
			new_char_list.append('<sil>')
			# tail token and end silence
			# new_char_list.append(char_list[-1])
			if num_frames - fire_place[-1] > START_END_THRESHOLD:
			_end = (num_frames + fire_place[-1]) * 0.5
			# _end = fire_place[-1]
			timestamp_list[-1][1] = _end*TIME_RATE
			timestamp_list.append([_endTIME_RATE, num_framesTIME_RATE])
			new_char_list.append("<sil>")
			else:
			timestamp_list[-1][1] = num_frames*TIME_RATE
			assert len(new_char_list) == len(timestamp_list)
			res_str = ""
			for char, timestamp in zip(new_char_list, timestamp_list):
			res_str += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
			res = []
			for char, timestamp in zip(char_list, timestamp_list):
			if char != '<sil>':
			res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
			return res_str, res


			class SpeechText2Timestamp:
			@@ -110,11 +54,11 @@
			assert check_argument_types()
			# 1. Build ASR model
			tp_model, tp_train_args = ASRTask.build_model_from_file(
			timestamp_infer_config, timestamp_model_file, device
			timestamp_infer_config, timestamp_model_file, device=device
			)
			if 'cuda' in device:
			tp_model = tp_model.cuda()

			tp_model = tp_model.cuda() # force model to cuda

			frontend = None
			if tp_train_args.frontend is not None:
			frontend = WavFrontend(cmvn_file=timestamp_cmvn_file, **tp_train_args.frontend_conf)
			@@ -172,8 +116,8 @@
			enc = enc[0]

			# c. Forward Predictor
			_, _, us_alphas, us_cif_peak = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
			return us_alphas, us_cif_peak
			_, _, us_alphas, us_peaks = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
			return us_alphas, us_peaks


			def inference(
			@@ -191,6 +135,8 @@
			dtype: str = "float32",
			seed: int = 0,
			num_workers: int = 1,
			split_with_space: bool = True,
			seg_dict_file: Optional[str] = None,
			**kwargs,
			):
			inference_pipeline = inference_modelscope(
			@@ -206,6 +152,8 @@
			dtype=dtype,
			seed=seed,
			num_workers=num_workers,
			split_with_space=split_with_space,
			seg_dict_file=seg_dict_file,
			**kwargs,
			)
			return inference_pipeline(data_path_and_name_and_type, raw_inputs)
			@@ -226,9 +174,14 @@
			dtype: str = "float32",
			seed: int = 0,
			num_workers: int = 1,
			split_with_space: bool = True,
			seg_dict_file: Optional[str] = None,
			**kwargs,
			):
			assert check_argument_types()
			ncpu = kwargs.get("ncpu", 1)
			torch.set_num_threads(ncpu)

			if batch_size > 1:
			raise NotImplementedError("batch decoding is not implemented")
			if ngpu > 1:
			@@ -256,6 +209,26 @@
			)
			logging.info("speechtext2timestamp_kwargs: {}".format(speechtext2timestamp_kwargs))
			speechtext2timestamp = SpeechText2Timestamp(**speechtext2timestamp_kwargs)

			preprocessor = LMPreprocessor(
			train=False,
			token_type=speechtext2timestamp.tp_train_args.token_type,
			token_list=speechtext2timestamp.tp_train_args.token_list,
			bpemodel=None,
			text_cleaner=None,
			g2p_type=None,
			text_name="text",
			non_linguistic_symbols=speechtext2timestamp.tp_train_args.non_linguistic_symbols,
			split_with_space=split_with_space,
			seg_dict_file=seg_dict_file,
			)

			if output_dir is not None:
			writer = DatadirWriter(output_dir)
			tp_writer = writer[f"timestamp_prediction"]
			# ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
			else:
			tp_writer = None

			def _forward(
			data_path_and_name_and_type,
			@@ -264,7 +237,14 @@
			fs: dict = None,
			param_dict: dict = None,
			**kwargs
			):
			):
			output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
			writer = None
			if output_path is not None:
			writer = DatadirWriter(output_path)
			tp_writer = writer[f"timestamp_prediction"]
			else:
			tp_writer = None
			# 3. Build data-iterator
			if data_path_and_name_and_type is None and raw_inputs is not None:
			if isinstance(raw_inputs, torch.Tensor):
			@@ -277,14 +257,11 @@
			batch_size=batch_size,
			key_file=key_file,
			num_workers=num_workers,
			preprocess_fn=ASRTask.build_preprocess_fn(speechtext2timestamp.tp_train_args, False),
			preprocess_fn=preprocessor,
			collate_fn=ASRTask.build_collate_fn(speechtext2timestamp.tp_train_args, False),
			allow_variable_data_keys=allow_variable_data_keys,
			inference=True,
			)

			finish_count = 0
			file_count = 1

			tp_result_list = []
			for keys, batch in loader:
			@@ -302,9 +279,13 @@
			for batch_id in range(_bs):
			key = keys[batch_id]
			token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
			ts_str, ts_list = time_stamp_lfr6_advance(us_alphas[batch_id], us_cif_peak[batch_id], token)
			ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token, force_time_shift=-3.0)
			logging.warning(ts_str)
			tp_result_list.append({'text':"".join([i for i in token if i != '<sil>']), 'timestamp': ts_list})
			item = {'key': key, 'value': ts_str, 'timestamp':ts_list}
			if tp_writer is not None:
			tp_writer["tp_sync"][key+'#'] = ts_str
			tp_writer["tp_time"][key+'#'] = str(ts_list)
			tp_result_list.append(item)
			return tp_result_list

			return _forward
			@@ -389,6 +370,18 @@
			default=1,
			help="The batch size for inference",
			)
			group.add_argument(
			"--seg_dict_file",
			type=str,
			default=None,
			help="The batch size for inference",
			)
			group.add_argument(
			"--split_with_space",
			type=bool,
			default=False,
			help="The batch size for inference",
			)

			return parser