Merge branch 'k2-fsa:master' into dev/k2ssl

egs/libricss/SURT/dprnn_zipformer/pretrained.py

@@ -0,0 +1,303 @@
+#!/usr/bin/env python3
+"""
+Usage:
+
+1. Download pre-trained models from
+https://huggingface.co/desh2608/icefall-surt-libricss-dprnn-zipformer
+
+2.
+
+./dprnn_zipformer/pretrained.py \
+  --checkpoint /path/to/pretrained.pt \
+  --tokens /path/to/data/lang_bpe_500/tokens.txt \
+  /path/to/foo.wav
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from beam_search import (
+    beam_search,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params, get_surt_model
+
+from icefall.utils import num_tokens
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        required=True,
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        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(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    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].contiguous())
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    token_table = k2.SymbolTable.from_file(params.tokens)
+
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = get_surt_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+    opts.mel_opts.high_freq = -400
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    B, T, F = features.shape
+    processed = model.mask_encoder(features)  # B,T,F*num_channels
+    masks = processed.view(B, T, F, params.num_channels).unbind(dim=-1)
+    x_masked = [features * m for m in masks]
+
+    # Recognition
+    # Concatenate the inputs along the batch axis
+    h = torch.cat(x_masked, dim=0)
+    h_lens = feature_lengths.repeat(params.num_channels)
+    encoder_out, encoder_out_lens = model.encoder(x=h, x_lens=h_lens)
+
+    if model.joint_encoder_layer is not None:
+        encoder_out = model.joint_encoder_layer(encoder_out)
+
+    def _group_channels(hyps: List[str]) -> List[List[str]]:
+        """
+        Currently we have a batch of size M*B, where M is the number of
+        channels and B is the batch size. We need to group the hypotheses
+        into B groups, each of which contains M hypotheses.
+
+        Example:
+            hyps = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2']
+            _group_channels(hyps) = [['a1', 'a2'], ['b1', 'b2'], ['c1', 'c2']]
+        """
+        assert len(hyps) == B * params.num_channels
+        out_hyps = []
+        for i in range(B):
+            out_hyps.append(hyps[i::B])
+        return out_hyps
+
+    hyps = []
+    msg = f"Using {params.method}"
+    logging.info(msg)
+
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
+    if params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+                hyps.append(token_ids_to_words(hyp))
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        s += f"{filename}:\n{hyp}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/libricss/SURT/dprnn_zipformer/train.py

@@ -62,9 +62,7 @@ from asr_datamodule import LibriCssAsrDataModule
 from decoder import Decoder
 from dprnn import DPRNN
 from einops.layers.torch import Rearrange
-from graph_pit.loss.optimized import optimized_graph_pit_mse_loss as gpit_mse
 from joiner import Joiner
-from lhotse.cut import Cut
 from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import LOG_EPSILON, fix_random_seed
 from model import SURT

egs/librispeech/ASR/zipformer/optim.py

@@ -787,7 +787,9 @@ class LRScheduler(object):
         is not the optimizer.
         """
         return {
-            "base_lrs": self.base_lrs,
+             # the user might try to override the base_lr, so don't include this in the state.
+             # previously they were included.
+             # "base_lrs": self.base_lrs,
             "epoch": self.epoch,
             "batch": self.batch,
         }
@@ -799,7 +801,12 @@ class LRScheduler(object):
             state_dict (dict): scheduler state. Should be an object returned
                 from a call to :meth:`state_dict`.
         """
+        # the things with base_lrs are a work-around for a previous problem
+        # where base_lrs were written with the state dict.
+        base_lrs = self.base_lrs
         self.__dict__.update(state_dict)
+        self.base_lrs = base_lrs
+
 
     def get_last_lr(self) -> List[float]:
         """Return last computed learning rate by current scheduler.  Will be a list of float."""