add pretrained to pruned_transducer_stateless2

egs/gigaspeech/ASR/conformer_ctc/pretrained.py

@@ -210,8 +210,7 @@ def get_parser():
         default="output",
         help="""
         Output directory name, we store output hypothesis 
-        """
+        """,
-
     )
 
     parser.add_argument(
@@ -220,7 +219,7 @@ def get_parser():
         default=10,
         help="""
         Number of input files in one batch, defaulted to 10
-        """
+        """,
     )
     parser.add_argument(
         "sound_files",
@@ -302,7 +301,7 @@ def decode_one_batch(
     feature: torch.Tensor,
     bpe_model: Optional[spm.SentencePieceProcessor] = None,
     G: Optional[k2.Fsa] = None,
-) -> Dict[str, List[List[str]]]:
+) -> List[List[str]]:
 
     device = decoding_graph.device
     assert feature.ndim == 3
@@ -404,11 +403,14 @@ def main():
     params.update(vars(args))
 
     su = ShortUUID(alphabet=string.ascii_lowercase + string.digits)
-    output_dir = Path(args.output_dir)/(su.random(length=5) + '-' + datetime.datetime.now().strftime(
+    output_dir = Path(args.output_dir) / (
-        "%Y-%m-%d_%H-%M"))
+        su.random(length=5)
+        + "-"
+        + datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")
+    )
     output_dir.mkdir(exist_ok=True, parents=True)
 
-    with (output_dir/"command").open("w") as f_cmd:
+    with (output_dir / "command").open("w") as f_cmd:
         f_cmd.write(f"{args}\n")
         f_cmd.write(f"{params}\n")
     logging.info(f"{params}")
@@ -502,23 +504,29 @@ def main():
     else:
         raise ValueError(f"Unsupported decoding method: {params.method}")
 
-    testdata = TestDataset(features)
+    inputdata = TestDataset(features)
-    tl = DataLoader(testdata, batch_size=args.batch_size)
+    tl = DataLoader(inputdata, batch_size=args.batch_size)
     hyps = []
     num_batches = len(tl)
 
     for batch_idx, batch in enumerate(tl):
-        hyps.extend(decode_one_batch(
+        hyps.extend(
-            params, model, decoding_graph, batch[0], bpe_model, G))
+            decode_one_batch(
+                params, model, decoding_graph, batch[0], bpe_model, G
+            )
+        )
         logging.info(
-            f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}")
+            f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}"
+        )
 
     logging.info(f"Writing hypothesis to output dir {output_dir}")
     s = "\n"
     for filename, hyp in zip(wave_names, hyps):
         words = " ".join(hyp)
         s += f"{filename}:\n{words}\n\n"
-        with (output_dir/os.path.basename(filename.replace(".wav", ".txt"))).open("w") as f_hyp:
+        with (
+            output_dir / os.path.basename(filename.replace(".wav", ".txt"))
+        ).open("w") as f_hyp:
             f_hyp.write(words)
     logging.info(s)
     logging.info("Decoding Done")

egs/gigaspeech/ASR/local/compute_fbank_gigaspeech_splits.py

@@ -59,9 +59,15 @@ def get_parser():
         "--num-splits",
         type=int,
         required=True,
-        help="The number of splits of the XL subset",
+        help="The number of splits of the subset",
     )
 
+    parser.add_argument(
+        "--subset",
+        type=str,
+        default="XL",
+        help="subset name, XL, L, S, XS"
+    )
     parser.add_argument(
         "--start",
         type=int,
@@ -80,7 +86,7 @@ def get_parser():
 
 def compute_fbank_gigaspeech_splits(args):
     num_splits = args.num_splits
-    output_dir = "data/fbank/XL_split"
+    output_dir = f"data/fbank/{args.subset}_split"
     output_dir = Path(output_dir)
     assert output_dir.exists(), f"{output_dir} does not exist!"
 
@@ -103,12 +109,12 @@ def compute_fbank_gigaspeech_splits(args):
         idx = f"{i + 1}".zfill(num_digits)
         logging.info(f"Processing {idx}/{num_splits}")
 
-        cuts_path = output_dir / f"cuts_XL.{idx}.jsonl.gz"
+        cuts_path = output_dir / f"cuts_{args.subset}.{idx}.jsonl.gz"
         if cuts_path.is_file():
             logging.info(f"{cuts_path} exists - skipping")
             continue
 
-        raw_cuts_path = output_dir / f"cuts_XL_raw.{idx}.jsonl.gz"
+        raw_cuts_path = output_dir / f"cuts_{args.subset}_raw.{idx}.jsonl.gz"
 
         logging.info(f"Loading {raw_cuts_path}")
         cut_set = CutSet.from_file(raw_cuts_path)
@@ -117,7 +123,7 @@ def compute_fbank_gigaspeech_splits(args):
 
         cut_set = cut_set.compute_and_store_features_batch(
             extractor=extractor,
-            storage_path=f"{output_dir}/feats_XL_{idx}",
+            storage_path=f"{output_dir}/feats_{args.subset}_{idx}",
             num_workers=args.num_workers,
             batch_duration=args.batch_duration,
         )

egs/gigaspeech/ASR/local/preprocess_gigaspeech.py

@@ -47,11 +47,7 @@ def preprocess_giga_speech():
     output_dir = Path("data/fbank")
     output_dir.mkdir(exist_ok=True)
 
-    dataset_parts = (
+    dataset_parts = "S"
-        "DEV",
-        "TEST",
-        "XL",
-    )
 
     logging.info("Loading manifest (may take 4 minutes)")
     manifests = read_manifests_if_cached(
@@ -86,16 +82,16 @@ def preprocess_giga_speech():
         )
         # Run data augmentation that needs to be done in the
         # time domain.
-        if partition not in ["DEV", "TEST"]:
+        # if partition not in ["DEV", "TEST"]:
-            logging.info(
+        #     logging.info(
-                f"Speed perturb for {partition} with factors 0.9 and 1.1 "
+        #         f"Speed perturb for {partition} with factors 0.9 and 1.1 "
-                "(Perturbing may take 8 minutes and saving may take 20 minutes)"
+        #         "(Perturbing may take 8 minutes and saving may take 20 minutes)"
-            )
+        #     )
-            cut_set = (
+        #     cut_set = (
-                cut_set
+        #         cut_set
-                + cut_set.perturb_speed(0.9)
+        #         + cut_set.perturb_speed(0.9)
-                + cut_set.perturb_speed(1.1)
+        #         + cut_set.perturb_speed(1.1)
-            )
+        #     )
         logging.info(f"Saving to {raw_cuts_path}")
         cut_set.to_file(raw_cuts_path)
 

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -503,34 +503,37 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    if params.iter > 0:
+    load_checkpoint(f"{params.exp_dir}/pretrained-iter-3488000-avg-20.pt", model)
-        filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+
-            : params.avg
+    # we don't average on models
-        ]
+    # if params.iter > 0:
-        if len(filenames) == 0:
+    #     filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-            raise ValueError(
+    #         : params.avg
-                f"No checkpoints found for"
+    #     ]
-                f" --iter {params.iter}, --avg {params.avg}"
+    #     if len(filenames) == 0:
-            )
+    #         raise ValueError(
-        elif len(filenames) < params.avg:
+    #             f"No checkpoints found for"
-            raise ValueError(
+    #             f" --iter {params.iter}, --avg {params.avg}"
-                f"Not enough checkpoints ({len(filenames)}) found for"
+    #         )
-                f" --iter {params.iter}, --avg {params.avg}"
+    #     elif len(filenames) < params.avg:
-            )
+    #         raise ValueError(
-        logging.info(f"averaging {filenames}")
+    #             f"Not enough checkpoints ({len(filenames)}) found for"
-        model.to(device)
+    #             f" --iter {params.iter}, --avg {params.avg}"
-        model.load_state_dict(average_checkpoints(filenames, device=device))
+    #         )
-    elif params.avg == 1:
+    #     logging.info(f"averaging {filenames}")
-        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    #     model.to(device)
-    else:
+    #     model.load_state_dict(average_checkpoints(filenames, device=device))
-        start = params.epoch - params.avg + 1
+    # elif params.avg == 1:
-        filenames = []
+    #     load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
-        for i in range(start, params.epoch + 1):
+    # else:
-            if start >= 0:
+    #     start = params.epoch - params.avg + 1
-                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+    #     filenames = []
-        logging.info(f"averaging {filenames}")
+    #     for i in range(start, params.epoch + 1):
-        model.to(device)
+    #         if start >= 0:
-        model.load_state_dict(average_checkpoints(filenames, device=device))
+    #             filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+    #     logging.info(f"averaging {filenames}")
+    #     model.to(device)
+    #     model.load_state_dict(average_checkpoints(filenames, device=device))
 
     model.to(device)
     model.eval()

egs/gigaspeech/ASR/pruned_transducer_stateless2/pretrained.py

@@ -0,0 +1,440 @@
+#!/usr/bin/env python3
+# Copyright      2021  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.
+"""
+Usage:
+
+(1) greedy search
+./pruned_transducer_stateless2/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method greedy_search \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(2) beam search
+./pruned_transducer_stateless2/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(3) modified beam search
+./pruned_transducer_stateless2/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method modified_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(4) fast beam search
+./pruned_transducer_stateless2/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method fast_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+You can also use `./pruned_transducer_stateless2/exp/epoch-xx.pt`.
+
+Note: ./pruned_transducer_stateless2/exp/pretrained.pt is generated by
+./pruned_transducer_stateless2/export.py
+"""
+
+import os
+import argparse
+import logging
+import math
+import string
+import datetime
+from pathlib import Path
+from typing import List
+from icefall.utils import setup_logger
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+import torchaudio
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from torch.nn.utils.rnn import pad_sequence
+from train import get_params, get_transducer_model
+from shortuuid import ShortUUID
+from icefall.utils import AttributeDict
+
+
+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(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_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.
+        """,
+    )
+
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="birch/output",
+        help="output directory name",
+    )
+
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=10,
+    )
+    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 = []
+    wave_names = []
+
+    def loadfile(filename):
+        wave, sample_rate = torchaudio.load(filename)
+        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])
+        wave_names.append(str(filename))
+
+    for f in filenames:
+        file_path = Path(f)
+        if file_path.is_file():
+            loadfile(file_path)
+        elif file_path.is_dir():
+            for filename in file_path.iterdir():
+                loadfile(filename)
+        else:
+            logging.error(f"{f} must be a filename or a dirname")
+    return ans, wave_names
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    features: torch.tensor,
+    sp: spm.SentencePieceProcessor,
+) -> List[List[str]]:
+
+    device = features.device
+    feature_lengths = [f.size(0) for f in features]
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lengths
+    )
+
+    num_waves = encoder_out.size(0)
+    hyps = []
+    msg = f"Using {params.method}"
+    if params.method == "beam_search":
+        msg += f" with beam size {params.beam_size}"
+    logging.debug(msg)
+
+    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 sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    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 sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    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 sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        for i in range(num_waves):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(f"Unsupported method: {params.method}")
+
+            hyps.append(sp.decode(hyp).split())
+    return hyps
+
+
+class TestDataset(Dataset):
+    def __init__(self, features: torch.Tensor):
+        self.features = features
+
+    def __len__(self):
+        return len(self.features)
+
+    def __getitem__(self, idx):
+        return (self.features[idx], 0)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+    su = ShortUUID(alphabet=string.ascii_lowercase + string.digits)
+
+    params.suffix = f"-pruned-transducer-stateless2-{params.method}"
+    if "fast_beam_search" in params.method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+    elif "beam_search" in params.method:
+        params.suffix += f"-beam-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+    output_dir = Path(params.output) / (
+        su.random(length=5)
+        + "-"
+        + datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")
+        + params.suffix
+    )
+    output_dir.mkdir(exist_ok=True, parents=True)
+    setup_logger(f"{output_dir}/log-decode")
+
+    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_transducer_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(params.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    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
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves, wavnames = 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)
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+    inputdata = TestDataset(features)
+    tl = DataLoader(inputdata, batch_size=params.batch_size)
+
+    num_batches = len(tl)
+    hyps = []
+    for batch_idx, batch in enumerate(tl):
+        hyps.extend(decode_one_batch(params, model, batch[0], sp))
+        logging.info(
+            f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}"
+        )
+
+    s = "\n"
+    assert len(wavnames) == len(hyps)
+    for filename, hyp in zip(wavnames, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+        with (
+            output_dir / os.path.basename(filename.replace(".wav", ".txt"))
+        ).open("w") as fhyp:
+            fhyp.write(words)
+
+    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()