icefall/egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py

#!/usr/bin/env python3
# flake8: noqa
# Copyright      2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
#
# 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 torchscript models exported by `torch.jit.script()`
and uses them to decode waves.
You can use the following command to get the exported models:

./zipformer/export.py \
  --exp-dir ./zipformer/exp \
  --causal 1 \
  --chunk-size 16 \
  --left-context-frames 128 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --epoch 30 \
  --avg 9 \
  --jit 1

Usage of this script:

./zipformer/jit_pretrained_streaming.py \
  --nn-model-filename ./zipformer/exp-causal/jit_script_chunk_16_left_128.pt \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  /path/to/foo.wav \
"""

import argparse
import logging
import math
from typing import List, Optional

import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
from torch.nn.utils.rnn import pad_sequence


def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--nn-model-filename",
        type=str,
        required=True,
        help="Path to the torchscript model cpu_jit.pt",
    )

    parser.add_argument(
        "--bpe-model",
        type=str,
        help="""Path to bpe.model.""",
    )

    parser.add_argument(
        "--sample-rate",
        type=int,
        default=16000,
        help="The sample rate of the input sound file",
    )

    parser.add_argument(
        "sound_file",
        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.",
    )

    return parser


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}. Given: {sample_rate}"
        # We use only the first channel
        ans.append(wave[0])
    return ans


def greedy_search(
    decoder: torch.jit.ScriptModule,
    joiner: torch.jit.ScriptModule,
    encoder_out: torch.Tensor,
    decoder_out: Optional[torch.Tensor] = None,
    hyp: Optional[List[int]] = None,
    device: torch.device = torch.device("cpu"),
):
    assert encoder_out.ndim == 2
    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.int32, device=device).unsqueeze(0)
        # decoder_input.shape (1,, 1 context_size)
        decoder_out = decoder(decoder_input, torch.tensor([False])).squeeze(1)
    else:
        assert decoder_out.ndim == 2
        assert hyp is not None, hyp

    T = encoder_out.size(0)
    for i in range(T):
        cur_encoder_out = encoder_out[i : i + 1]
        joiner_out = joiner(cur_encoder_out, decoder_out).squeeze(0)
        y = joiner_out.argmax(dim=0).item()

        if y != blank_id:
            hyp.append(y)
            decoder_input = hyp[-context_size:]

            decoder_input = torch.tensor(
                decoder_input, dtype=torch.int32, device=device
            ).unsqueeze(0)
            decoder_out = decoder(decoder_input, torch.tensor([False])).squeeze(1)

    return hyp, decoder_out


def create_streaming_feature_extractor(sample_rate) -> 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 = sample_rate
    opts.mel_opts.num_bins = 80
    return OnlineFbank(opts)


@torch.no_grad()
def main():
    parser = get_parser()
    args = parser.parse_args()
    logging.info(vars(args))

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", 0)

    logging.info(f"device: {device}")

    model = torch.jit.load(args.nn_model_filename)
    model.eval()
    model.to(device)

    encoder = model.encoder
    decoder = model.decoder
    joiner = model.joiner

    sp = spm.SentencePieceProcessor()
    sp.load(args.bpe_model)

    logging.info("Constructing Fbank computer")
    online_fbank = create_streaming_feature_extractor(args.sample_rate)

    logging.info(f"Reading sound files: {args.sound_file}")
    wave_samples = read_sound_files(
        filenames=[args.sound_file],
        expected_sample_rate=args.sample_rate,
    )[0]
    logging.info(wave_samples.shape)

    logging.info("Decoding started")

    chunk_length = encoder.chunk_size * 2
    T = chunk_length + encoder.pad_length

    logging.info(f"chunk_length: {chunk_length}")
    logging.info(f"T: {T}")

    states = encoder.get_init_states(device=device)

    tail_padding = torch.zeros(int(0.3 * args.sample_rate), dtype=torch.float32)

    wave_samples = torch.cat([wave_samples, tail_padding])

    chunk = int(0.25 * args.sample_rate)  # 0.2 second
    num_processed_frames = 0

    hyp = None
    decoder_out = None

    start = 0
    while start < wave_samples.numel():
        logging.info(f"{start}/{wave_samples.numel()}")
        end = min(start + chunk, wave_samples.numel())
        samples = wave_samples[start:end]
        start += chunk
        online_fbank.accept_waveform(
            sampling_rate=args.sample_rate,
            waveform=samples,
        )
        while online_fbank.num_frames_ready - num_processed_frames >= T:
            frames = []
            for i in range(T):
                frames.append(online_fbank.get_frame(num_processed_frames + i))
            frames = torch.cat(frames, dim=0).to(device).unsqueeze(0)
            x_lens = torch.tensor([T], dtype=torch.int32, device=device)
            encoder_out, out_lens, states = encoder(
                features=frames,
                feature_lengths=x_lens,
                states=states,
            )
            num_processed_frames += chunk_length

            hyp, decoder_out = greedy_search(
                decoder, joiner, encoder_out.squeeze(0), decoder_out, hyp, device=device
            )

    context_size = 2
    logging.info(args.sound_file)
    logging.info(sp.decode(hyp[context_size:]))

    logging.info("Decoding Done")


torch.set_num_threads(4)
torch.set_num_interop_threads(1)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._set_graph_executor_optimize(False)
if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)
    main()