python/FunASR-XL.git

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			#!/bin/bash
			#
			# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
			#
			# Licensed under the Apache License, Version 2.0 (the "License");
			# you may not use this file except in compliance with the License.
			# You may obtain a copy of the License at
			#
			# http://www.apache.org/licenses/LICENSE-2.0
			#
			# Unless required by applicable law or agreed to in writing, software
			# distributed under the License is distributed on an "AS IS" BASIS,
			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
			# See the License for the specific language governing permissions and
			# limitations under the License.
			import math
			import triton_python_backend_utils as pb_utils
			from torch.utils.dlpack import to_dlpack
			import torch
			import numpy as np
			import kaldifeat
			import _kaldifeat
			from typing import List
			import json
			import yaml
			from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union

			class LFR(torch.nn.Module):
			"""Batch LFR: https://github.com/Mddct/devil-asr/blob/main/patch/lfr.py """
			def __init__(self, m: int = 7, n: int = 6) -> None:
			"""
			Actually, this implements stacking frames and skipping frames.
			if m = 1 and n = 1, just return the origin features.
			if m = 1 and n > 1, it works like skipping.
			if m > 1 and n = 1, it works like stacking but only support right frames.
			if m > 1 and n > 1, it works like LFR.
			"""
			super().__init__()

			self.m = m
			self.n = n

			self.left_padding_nums = math.ceil((self.m - 1) // 2)

			def forward(self, input_tensor: torch.Tensor,
			input_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
			B, _, D = input_tensor.size()
			n_lfr = torch.ceil(input_lens / self.n)

			prepad_nums = input_lens + self.left_padding_nums

			right_padding_nums = torch.where(
			self.m >= (prepad_nums - self.n * (n_lfr - 1)),
			self.m - (prepad_nums - self.n * (n_lfr - 1)),
			0,
			)

			T_all = self.left_padding_nums + input_lens + right_padding_nums

			new_len = T_all // self.n

			T_all_max = T_all.max().int()

			tail_frames_index = (input_lens - 1).view(B, 1, 1).repeat(1, 1, D) # [B,1,D]

			tail_frames = torch.gather(input_tensor, 1, tail_frames_index)
			tail_frames = tail_frames.repeat(1, right_padding_nums.max().int(), 1)
			head_frames = input_tensor[:, 0:1, :].repeat(1, self.left_padding_nums, 1)

			# stack
			input_tensor = torch.cat([head_frames, input_tensor, tail_frames], dim=1)

			index = torch.arange(T_all_max,
			device=input_tensor.device,
			dtype=input_lens.dtype).unsqueeze(0).repeat(B, 1) # [B, T_all_max]
			index_mask = (index <
			(self.left_padding_nums + input_lens).unsqueeze(1)
			) #[B, T_all_max]

			tail_index_mask = torch.logical_not(
			index >= (T_all.unsqueeze(1))) & index_mask
			tail = torch.ones(T_all_max,
			dtype=input_lens.dtype,
			device=input_tensor.device).unsqueeze(0).repeat(B, 1) * (
			T_all_max - 1) # [B, T_all_max]
			indices = torch.where(torch.logical_or(index_mask, tail_index_mask),
			index, tail)
			input_tensor = torch.gather(input_tensor, 1, indices.unsqueeze(2).repeat(1, 1, D))

			input_tensor = input_tensor.unfold(1, self.m, step=self.n).transpose(2, 3)

			return input_tensor.reshape(B, -1, D * self.m), new_len

			class WavFrontend():
			"""Conventional frontend structure for ASR.
			"""

			def __init__(
			self,
			cmvn_file: str = None,
			fs: int = 16000,
			window: str = 'hamming',
			n_mels: int = 80,
			frame_length: int = 25,
			frame_shift: int = 10,
			filter_length_min: int = -1,
			filter_length_max: float = -1,
			lfr_m: int = 7,
			lfr_n: int = 6,
			dither: float = 1.0
			) -> None:
			# check_argument_types()

			self.fs = fs
			self.window = window
			self.n_mels = n_mels
			self.frame_length = frame_length
			self.frame_shift = frame_shift
			self.filter_length_min = filter_length_min
			self.filter_length_max = filter_length_max
			self.lfr_m = lfr_m
			self.lfr_n = lfr_n
			self.lfr = LFR(lfr_m, lfr_n)
			self.cmvn_file = cmvn_file
			self.dither = dither

			if self.cmvn_file:
			self.cmvn = self.load_cmvn()

			def apply_cmvn_batch(self, inputs: np.ndarray) -> np.ndarray:
			"""
			Apply CMVN with mvn data
			"""
			batch, frame, dim = inputs.shape
			means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
			vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))

			means = torch.from_numpy(means).to(inputs.device)
			vars = torch.from_numpy(vars).to(inputs.device)
			# print(inputs.shape, means.shape, vars.shape)
			inputs = (inputs + means) * vars
			return inputs

			def load_cmvn(self,) -> np.ndarray:
			with open(self.cmvn_file, 'r', encoding='utf-8') as f:
			lines = f.readlines()

			means_list = []
			vars_list = []
			for i in range(len(lines)):
			line_item = lines[i].split()
			if line_item[0] == '<AddShift>':
			line_item = lines[i + 1].split()
			if line_item[0] == '<LearnRateCoef>':
			add_shift_line = line_item[3:(len(line_item) - 1)]
			means_list = list(add_shift_line)
			continue
			elif line_item[0] == '<Rescale>':
			line_item = lines[i + 1].split()
			if line_item[0] == '<LearnRateCoef>':
			rescale_line = line_item[3:(len(line_item) - 1)]
			vars_list = list(rescale_line)
			continue

			means = np.array(means_list).astype(np.float64)
			vars = np.array(vars_list).astype(np.float64)
			cmvn = np.array([means, vars])
			return cmvn


			class Fbank(torch.nn.Module):
			def __init__(self, opts):
			super(Fbank, self).__init__()
			self.fbank = kaldifeat.Fbank(opts)

			def forward(self, waves: List[torch.Tensor]):
			return self.fbank(waves)


			class TritonPythonModel:
			"""Your Python model must use the same class name. Every Python model
			that is created must have "TritonPythonModel" as the class name.
			"""

			def initialize(self, args):
			"""`initialize` is called only once when the model is being loaded.
			Implementing `initialize` function is optional. This function allows
			the model to initialize any state associated with this model.

			Parameters
			----------
			args : dict
			Both keys and values are strings. The dictionary keys and values are:
			* model_config: A JSON string containing the model configuration
			* model_instance_kind: A string containing model instance kind
			* model_instance_device_id: A string containing model instance device ID
			* model_repository: Model repository path
			* model_version: Model version
			* model_name: Model name
			"""
			self.model_config = model_config = json.loads(args['model_config'])
			self.max_batch_size = max(model_config["max_batch_size"], 1)
			self.device = "cuda"

			# Get OUTPUT0 configuration
			output0_config = pb_utils.get_output_config_by_name(
			model_config, "speech")
			# Convert Triton types to numpy types
			output0_dtype = pb_utils.triton_string_to_numpy(
			output0_config['data_type'])

			if output0_dtype == np.float32:
			self.output0_dtype = torch.float32
			else:
			self.output0_dtype = torch.float16

			# Get OUTPUT1 configuration
			output1_config = pb_utils.get_output_config_by_name(
			model_config, "speech_lengths")
			# Convert Triton types to numpy types
			self.output1_dtype = pb_utils.triton_string_to_numpy(
			output1_config['data_type'])

			params = self.model_config['parameters']

			for li in params.items():
			key, value = li
			value = value["string_value"]
			if key == "config_path":
			with open(str(value), 'rb') as f:
			config = yaml.load(f, Loader=yaml.Loader)
			if key == "cmvn_path":
			cmvn_path = str(value)

			opts = kaldifeat.FbankOptions()
			opts.frame_opts.dither = 1.0 # TODO: 0.0 or 1.0
			opts.frame_opts.window_type = config['frontend_conf']['window']
			opts.mel_opts.num_bins = int(config['frontend_conf']['n_mels'])
			opts.frame_opts.frame_shift_ms = float(config['frontend_conf']['frame_shift'])
			opts.frame_opts.frame_length_ms = float(config['frontend_conf']['frame_length'])
			opts.frame_opts.samp_freq = int(config['frontend_conf']['fs'])
			opts.device = torch.device(self.device)
			self.opts = opts
			self.feature_extractor = Fbank(self.opts)
			self.feature_size = opts.mel_opts.num_bins

			self.frontend = WavFrontend(
			cmvn_file=cmvn_path,
			**config['frontend_conf'])

			def extract_feat(self,
			waveform_list: List[np.ndarray]
			) -> Tuple[np.ndarray, np.ndarray]:
			feats, feats_len = [], []
			wavs = []
			for waveform in waveform_list:
			wav = torch.from_numpy(waveform).float().squeeze().to(self.device)
			wavs.append(wav)

			features = self.feature_extractor(wavs)
			features_len = [feature.shape[0] for feature in features]
			speech = torch.zeros((len(features), max(features_len), self.opts.mel_opts.num_bins),
			dtype=self.output0_dtype, device=self.device)
			for i, feature in enumerate(features):
			speech[i,:int(features_len[i])] = feature
			speech_lens = torch.tensor(features_len,dtype=torch.int64).to(self.device)

			feats, feats_len = self.frontend.lfr(speech, speech_lens)
			feats_len = feats_len.type(torch.int32)

			feats = self.frontend.apply_cmvn_batch(feats)
			feats = feats.type(self.output0_dtype)

			return feats, feats_len

			def execute(self, requests):
			"""`execute` must be implemented in every Python model. `execute`
			function receives a list of pb_utils.InferenceRequest as the only
			argument. This function is called when an inference is requested
			for this model.

			Parameters
			----------
			requests : list
			A list of pb_utils.InferenceRequest

			Returns
			-------
			list
			A list of pb_utils.InferenceResponse. The length of this list must
			be the same as `requests`
			"""
			batch_count = []
			total_waves = []
			batch_len = []
			responses = []
			for request in requests:

			input0 = pb_utils.get_input_tensor_by_name(request, "wav")
			input1 = pb_utils.get_input_tensor_by_name(request, "wav_lens")

			cur_b_wav = input0.as_numpy() * (1 << 15) # b x -1
			total_waves.append(cur_b_wav)

			features, feats_len = self.extract_feat(total_waves)

			for i in range(features.shape[0]):
			speech = features[i:i+1][:int(feats_len[i].cpu())]
			speech_lengths = feats_len[i].unsqueeze(0).unsqueeze(0)

			speech, speech_lengths = speech.cpu(), speech_lengths.cpu()
			out0 = pb_utils.Tensor.from_dlpack("speech", to_dlpack(speech))
			out1 = pb_utils.Tensor.from_dlpack("speech_lengths",
			to_dlpack(speech_lengths))
			inference_response = pb_utils.InferenceResponse(output_tensors=[out0, out1])
			responses.append(inference_response)
			return responses