Zipformer recipe for SPGISpeech (#1449)

egs/spgispeech/ASR/RESULTS.md

@@ -1,5 +1,70 @@
 ## Results
 
+### SPGISpeech BPE training results (Zipformer Transducer)
+
+#### 2024-01-05
+
+#### Zipformer encoder + embedding decoder
+
+Transducer: Zipformer encoder + stateless decoder.
+
+The WERs are:
+
+|                           | dev | val | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             | 2.08       | 2.14       | --epoch 30 --avg 10 |
+| modified beam search      | 2.05       | 2.09       | --epoch 30 --avg 10 --beam-size 4 |
+| fast beam search          | 2.07       | 2.17       | --epoch 30 --avg 10 --beam 20 --max-contexts 8 --max-states 64 |
+
+**NOTE:** SPGISpeech transcripts can be prepared in `ortho` or `norm` ways, which refer to whether the
+transcripts are orthographic or normalized. These WERs correspond to the normalized transcription
+scenario.
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+python zipformer/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --num-workers 2 \
+  --max-duration 1000
+```
+
+The decoding command is:
+```
+# greedy search
+python ./zipformer/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir ./zipformer/exp \
+  --max-duration 1000 \
+  --decoding-method greedy_search
+
+# modified beam search
+python ./zipformer/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir ./zipformer/exp \
+  --max-duration 1000 \
+  --decoding-method modified_beam_search
+
+# fast beam search
+python ./zipformer/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir ./zipformer/exp \
+  --max-duration 1000 \
+  --decoding-method fast_beam_search \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
+```
+
 ### SPGISpeech BPE training results (Pruned Transducer)
 
 #### 2022-05-11
@@ -43,28 +108,28 @@ The decoding command is:
 ```
 # greedy search
 ./pruned_transducer_stateless2/decode.py \
-        --iter 696000 --avg 10 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 100 \
-        --decoding-method greedy_search
+  --iter 696000 --avg 10 \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --max-duration 100 \
+  --decoding-method greedy_search
 
 # modified beam search
 ./pruned_transducer_stateless2/decode.py \
-        --iter 696000 --avg 10 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 100 \
-        --decoding-method modified_beam_search \
-        --beam-size 4
+  --iter 696000 --avg 10 \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --max-duration 100 \
+  --decoding-method modified_beam_search \
+  --beam-size 4
 
 # fast beam search
 ./pruned_transducer_stateless2/decode.py \
-        --iter 696000 --avg 10 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 1500 \
-        --decoding-method fast_beam_search \
-        --beam 4 \
-        --max-contexts 4 \
-        --max-states 8
+  --iter 696000 --avg 10 \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --max-duration 1500 \
+  --decoding-method fast_beam_search \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
 ```
 
 Pretrained model is available at <https://huggingface.co/desh2608/icefall-asr-spgispeech-pruned-transducer-stateless2>

egs/spgispeech/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -102,6 +102,20 @@ class SPGISpeechAsrDataModule:
             help="Determines the maximum duration of a concatenated cut "
             "relative to the duration of the longest cut in a batch.",
         )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=False,
+            help="When enabled, the last batch will be dropped",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
         group.add_argument(
             "--gap",
             type=float,
@@ -143,7 +157,7 @@ class SPGISpeechAsrDataModule:
         group.add_argument(
             "--num-workers",
             type=int,
-            default=8,
+            default=2,
             help="The number of training dataloader workers that "
             "collect the batches.",
         )
@@ -176,7 +190,7 @@ class SPGISpeechAsrDataModule:
             The state dict for the training sampler.
         """
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.manifest_dir / "cuts_musan.jsonl.gz")
+        cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
 
         transforms = []
         if self.args.enable_musan:
@@ -223,11 +237,13 @@ class SPGISpeechAsrDataModule:
                 cut_transforms=transforms,
                 input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
                 input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
             )
         else:
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
                 input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
             )
 
         logging.info("Using DynamicBucketingSampler.")
@@ -276,10 +292,12 @@ class SPGISpeechAsrDataModule:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
                 input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                return_cuts=self.args.return_cuts,
             )
         else:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
             )
         valid_sampler = DynamicBucketingSampler(
             cuts_valid,
@@ -303,6 +321,7 @@ class SPGISpeechAsrDataModule:
             input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
             if self.args.on_the_fly_feats
             else PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(
             cuts, max_duration=self.args.max_duration, shuffle=False

egs/spgispeech/ASR/zipformer/asr_datamodule.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/asr_datamodule.py

egs/spgispeech/ASR/zipformer/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py

egs/spgispeech/ASR/zipformer/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decoder.py

egs/spgispeech/ASR/zipformer/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/transducer_stateless/encoder_interface.py

egs/spgispeech/ASR/zipformer/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/joiner.py

egs/spgispeech/ASR/zipformer/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/model.py

egs/spgispeech/ASR/zipformer/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/optim.py

egs/spgispeech/ASR/zipformer/pretrained.py

@@ -0,0 +1,382 @@
+#!/usr/bin/env python3
+# Copyright      2021-2023  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 a checkpoint and uses it to decode waves.
+You can generate the checkpoint with the following command:
+
+Note: This is a example for spgispeech dataset, if you are using different
+dataset, you should change the argument values according to your dataset.
+
+- For non-streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+- For streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --causal 1 \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+Usage of this script:
+
+- For non-streaming model:
+
+(1) greedy search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --method greedy_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(2) modified beam search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens ./data/lang_bpe_500/tokens.txt \
+  --method modified_beam_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(3) fast beam search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens ./data/lang_bpe_500/tokens.txt \
+  --method fast_beam_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+- For streaming model:
+
+(1) greedy search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --causal 1 \
+  --chunk-size 16 \
+  --left-context-frames 128 \
+  --tokens ./data/lang_bpe_500/tokens.txt \
+  --method greedy_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(2) modified beam search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --causal 1 \
+  --chunk-size 16 \
+  --left-context-frames 128 \
+  --tokens ./data/lang_bpe_500/tokens.txt \
+  --method modified_beam_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(3) fast beam search
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --causal 1 \
+  --chunk-size 16 \
+  --left-context-frames 128 \
+  --tokens ./data/lang_bpe_500/tokens.txt \
+  --method fast_beam_search \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+
+You can also use `./zipformer/exp/epoch-xx.pt`.
+
+Note: ./zipformer/exp/pretrained.pt is generated by ./zipformer/export.py
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from beam_search import (
+    fast_beam_search_one_best,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from export import num_tokens
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_model, get_params
+
+from icefall.utils import make_pad_mask
+
+
+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,
+        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(
+        "--beam",
+        type=float,
+        default=4,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_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}")
+
+    if params.causal:
+        assert (
+            "," not in params.chunk_size
+        ), "chunk_size should be one value in decoding."
+        assert (
+            "," not in params.left_context_frames
+        ), "left_context_frames should be one value in decoding."
+
+    logging.info("Creating model")
+    model = get_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)
+
+    # model forward
+    encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)
+
+    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.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
+    elif params.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))
+    elif params.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))
+    else:
+        raise ValueError(f"Unsupported method: {params.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/spgispeech/ASR/zipformer/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling.py

egs/spgispeech/ASR/zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling_converter.py

egs/spgispeech/ASR/zipformer/subsampling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/subsampling.py

egs/spgispeech/ASR/zipformer/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/zipformer.py