#!/usr/bin/env python3 # Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) # # See ../../../../LICENSE for clarification regarding multiple authors # # 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. """ This script loads ONNX models and uses them to decode waves. You can use the following command to get the exported models: ./lstm_transducer_stateless2/export.py \ --exp-dir ./lstm_transducer_stateless2/exp \ --bpe-model data/lang_bpe_500/bpe.model \ --epoch 20 \ --avg 10 \ --onnx 1 Usage of this script: ./lstm_transducer_stateless2/onnx-streaming-decode.py \ --encoder-model-filename ./lstm_transducer_stateless2/exp/encoder.onnx \ --decoder-model-filename ./lstm_transducer_stateless2/exp/decoder.onnx \ --joiner-model-filename ./lstm_transducer_stateless2/exp/joiner.onnx \ --joiner-encoder-proj-model-filename ./lstm_transducer_stateless2/exp/joiner_encoder_proj.onnx \ --joiner-decoder-proj-model-filename ./lstm_transducer_stateless2/exp/joiner_decoder_proj.onnx \ --bpe-model ./data/lang_bpe_500/bpe.model \ /path/to/foo.wav \ /path/to/bar.wav """ import argparse import logging from typing import List, Optional, Tuple import onnxruntime as ort import sentencepiece as spm import torch import torchaudio from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature def get_args(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument( "--bpe-model-filename", type=str, help="Path to bpe.model", ) parser.add_argument( "--encoder-model-filename", type=str, required=True, help="Path to the encoder onnx model. ", ) parser.add_argument( "--decoder-model-filename", type=str, required=True, help="Path to the decoder onnx model. ", ) parser.add_argument( "--joiner-model-filename", type=str, required=True, help="Path to the joiner onnx model. ", ) parser.add_argument( "--joiner-encoder-proj-model-filename", type=str, required=True, help="Path to the joiner encoder_proj onnx model. ", ) parser.add_argument( "--joiner-decoder-proj-model-filename", type=str, required=True, help="Path to the joiner decoder_proj onnx model. ", ) parser.add_argument( "--bpe-model", type=str, help="""Path to bpe.model.""", ) parser.add_argument( "sound_filename", type=str, help="The input sound file(s) to transcribe. " "Supported formats are those supported by torchaudio.load(). " "For example, wav and flac are supported. " "The sample rate has to be 16kHz.", ) parser.add_argument( "--sample-rate", type=int, default=16000, help="The sample rate of the input sound file", ) parser.add_argument( "--context-size", type=int, default=2, help="Context size of the decoder model", ) return parser.parse_args() def read_sound_files( filenames: List[str], expected_sample_rate: float ) -> List[torch.Tensor]: """Read a list of sound files into a list 1-D float32 torch tensors. Args: filenames: A list of sound filenames. expected_sample_rate: The expected sample rate of the sound files. Returns: Return a list of 1-D float32 torch tensors. """ ans = [] for f in filenames: wave, sample_rate = torchaudio.load(f) assert sample_rate == expected_sample_rate, ( f"expected sample rate: {expected_sample_rate}. " f"Given: {sample_rate}" ) # We use only the first channel ans.append(wave[0]) return ans class Model: def __init__(self, args): session_opts = ort.SessionOptions() session_opts.inter_op_num_threads = 5 session_opts.intra_op_num_threads = 5 self.session_opts = session_opts self.init_encoder(args) self.init_decoder(args) self.init_joiner(args) self.init_joiner_encoder_proj(args) self.init_joiner_decoder_proj(args) def init_encoder(self, args): self.encoder = ort.InferenceSession( args.encoder_model_filename, sess_options=self.session_opts, ) def init_decoder(self, args): self.decoder = ort.InferenceSession( args.decoder_model_filename, sess_options=self.session_opts, ) def init_joiner(self, args): self.joiner = ort.InferenceSession( args.joiner_model_filename, sess_options=self.session_opts, ) def init_joiner_encoder_proj(self, args): self.joiner_encoder_proj = ort.InferenceSession( args.joiner_encoder_proj_model_filename, sess_options=self.session_opts, ) def init_joiner_decoder_proj(self, args): self.joiner_decoder_proj = ort.InferenceSession( args.joiner_decoder_proj_model_filename, sess_options=self.session_opts, ) def run_encoder( self, x, h0, c0 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Args: x: A tensor of shape (N, T, C) h0: A tensor of shape (num_layers, N, proj_size) c0: A tensor of shape (num_layers, N, hidden_size) Returns: Return a tuple containing: - encoder_out: A tensor of shape (N, T', C') - next_h0: A tensor of shape (num_layers, N, proj_size) - next_c0: A tensor of shape (num_layers, N, hidden_size) """ encoder_input_nodes = self.encoder.get_inputs() encoder_out_nodes = self.encoder.get_outputs() x_lens = torch.tensor([x.size(1)], dtype=torch.int64) encoder_out, encoder_out_lens, next_h0, next_c0 = self.encoder.run( [ encoder_out_nodes[0].name, encoder_out_nodes[1].name, encoder_out_nodes[2].name, encoder_out_nodes[3].name, ], { encoder_input_nodes[0].name: x.numpy(), encoder_input_nodes[1].name: x_lens.numpy(), encoder_input_nodes[2].name: h0.numpy(), encoder_input_nodes[3].name: c0.numpy(), }, ) return ( torch.from_numpy(encoder_out), torch.from_numpy(next_h0), torch.from_numpy(next_c0), ) def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor: """ Args: decoder_input: A tensor of shape (N, context_size). Its dtype is torch.int64. Returns: Return a tensor of shape (N, 1, decoder_out_dim). """ decoder_input_nodes = self.decoder.get_inputs() decoder_output_nodes = self.decoder.get_outputs() decoder_out = self.decoder.run( [decoder_output_nodes[0].name], { decoder_input_nodes[0].name: decoder_input.numpy(), }, )[0] return self.run_joiner_decoder_proj( torch.from_numpy(decoder_out).squeeze(1) ) def run_joiner( self, projected_encoder_out: torch.Tensor, projected_decoder_out: torch.Tensor, ) -> torch.Tensor: """ Args: projected_encoder_out: A tensor of shape (N, joiner_dim) projected_decoder_out: A tensor of shape (N, joiner_dim) Returns: Return a tensor of shape (N, vocab_size) """ joiner_input_nodes = self.joiner.get_inputs() joiner_output_nodes = self.joiner.get_outputs() logits = self.joiner.run( [joiner_output_nodes[0].name], { joiner_input_nodes[0].name: projected_encoder_out.numpy(), joiner_input_nodes[1].name: projected_decoder_out.numpy(), }, )[0] return torch.from_numpy(logits) def run_joiner_encoder_proj( self, encoder_out: torch.Tensor, ) -> torch.Tensor: """ Args: encoder_out: A tensor of shape (N, encoder_out_dim) Returns: A tensor of shape (N, joiner_dim) """ projected_encoder_out = self.joiner_encoder_proj.run( [self.joiner_encoder_proj.get_outputs()[0].name], { self.joiner_encoder_proj.get_inputs()[ 0 ].name: encoder_out.numpy() }, )[0] return torch.from_numpy(projected_encoder_out) def run_joiner_decoder_proj( self, decoder_out: torch.Tensor, ) -> torch.Tensor: """ Args: decoder_out: A tensor of shape (N, decoder_out_dim) Returns: A tensor of shape (N, joiner_dim) """ projected_decoder_out = self.joiner_decoder_proj.run( [self.joiner_decoder_proj.get_outputs()[0].name], { self.joiner_decoder_proj.get_inputs()[ 0 ].name: decoder_out.numpy() }, )[0] return torch.from_numpy(projected_decoder_out) def create_streaming_feature_extractor() -> OnlineFeature: """Create a CPU streaming feature extractor. At present, we assume it returns a fbank feature extractor with fixed options. In the future, we will support passing in the options from outside. Returns: Return a CPU streaming feature extractor. """ opts = FbankOptions() opts.device = "cpu" opts.frame_opts.dither = 0 opts.frame_opts.snip_edges = False opts.frame_opts.samp_freq = 16000 opts.mel_opts.num_bins = 80 return OnlineFbank(opts) def greedy_search( model: Model, encoder_out: torch.Tensor, decoder_out: Optional[torch.Tensor] = None, hyp: Optional[List[int]] = None, ): assert encoder_out.ndim == 2 assert encoder_out.shape[0] == 1, "TODO: support batch_size > 1" context_size = 2 blank_id = 0 if decoder_out is None: assert hyp is None, hyp hyp = [blank_id] * context_size decoder_input = torch.tensor( [hyp], dtype=torch.int64 ) # (1, context_size) decoder_out = model.run_decoder(decoder_input) else: assert decoder_out.shape[0] == 1 assert hyp is not None, hyp projected_encoder_out = model.run_joiner_encoder_proj(encoder_out) joiner_out = model.run_joiner(projected_encoder_out, decoder_out) y = joiner_out.squeeze(0).argmax(dim=0).item() if y != blank_id: hyp.append(y) decoder_input = hyp[-context_size:] decoder_input = torch.tensor([decoder_input], dtype=torch.int64) decoder_out = model.run_decoder(decoder_input) return hyp, decoder_out def main(): args = get_args() logging.info(vars(args)) model = Model(args) sound_file = args.sound_filename sample_rate = 16000 sp = spm.SentencePieceProcessor() sp.load(args.bpe_model_filename) logging.info("Constructing Fbank computer") online_fbank = create_streaming_feature_extractor() logging.info(f"Reading sound files: {sound_file}") wave_samples = read_sound_files( filenames=[sound_file], expected_sample_rate=sample_rate, )[0] logging.info(wave_samples.shape) num_encoder_layers = 12 batch_size = 1 d_model = 512 rnn_hidden_size = 1024 h0 = torch.zeros(num_encoder_layers, batch_size, d_model) c0 = torch.zeros(num_encoder_layers, batch_size, rnn_hidden_size) hyp = None decoder_out = None num_processed_frames = 0 segment = 9 offset = 4 chunk = 3200 # 0.2 second start = 0 while start < wave_samples.numel(): end = min(start + chunk, wave_samples.numel()) samples = wave_samples[start:end] start += chunk online_fbank.accept_waveform( sampling_rate=sample_rate, waveform=samples, ) while online_fbank.num_frames_ready - num_processed_frames >= segment: frames = [] for i in range(segment): frames.append(online_fbank.get_frame(num_processed_frames + i)) num_processed_frames += offset frames = torch.cat(frames, dim=0).unsqueeze(0) encoder_out, h0, c0 = model.run_encoder(frames, h0, c0) hyp, decoder_out = greedy_search( model, encoder_out.squeeze(0), decoder_out, hyp ) online_fbank.accept_waveform( sampling_rate=sample_rate, waveform=torch.zeros(5000, dtype=torch.float) ) online_fbank.input_finished() while online_fbank.num_frames_ready - num_processed_frames >= segment: frames = [] for i in range(segment): frames.append(online_fbank.get_frame(num_processed_frames + i)) num_processed_frames += offset frames = torch.cat(frames, dim=0).unsqueeze(0) encoder_out, h0, c0 = model.run_encoder(frames, h0, c0) hyp, decoder_out = greedy_search( model, encoder_out.squeeze(0), decoder_out, hyp ) context_size = 2 logging.info(sound_file) logging.info(sp.decode(hyp[context_size:])) if __name__ == "__main__": formatter = ( "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" ) logging.basicConfig(format=formatter, level=logging.INFO) main()