Add modified-beam-search and fast-beam-search

egs/aishell/ASR/pruned_transducer_stateless/beam_search.py

@@ -17,10 +17,91 @@
 from dataclasses import dataclass
 from typing import Dict, List, Optional
 
-import numpy as np
+import k2
 import torch
 from model import Transducer
 
+from icefall.decode import one_best_decoding
+from icefall.utils import get_texts
+
+
+def fast_beam_search(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+    Returns:
+      Return the decoded result.
+    """
+    assert encoder_out.ndim == 3
+
+    context_size = model.decoder.context_size
+    vocab_size = model.decoder.vocab_size
+
+    B, T, C = encoder_out.shape
+
+    config = k2.RnntDecodingConfig(
+        vocab_size=vocab_size,
+        decoder_history_len=context_size,
+        beam=beam,
+        max_contexts=max_contexts,
+        max_states=max_states,
+    )
+    individual_streams = []
+    for i in range(B):
+        individual_streams.append(k2.RnntDecodingStream(decoding_graph))
+    decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
+
+    for t in range(T):
+        # shape is a RaggedShape of shape (B, context)
+        # contexts is a Tensor of shape (shape.NumElements(), context_size)
+        shape, contexts = decoding_streams.get_contexts()
+        # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
+        contexts = contexts.to(torch.int64)
+        # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
+        decoder_out = model.decoder(contexts, need_pad=False)
+        # current_encoder_out is of shape
+        # (shape.NumElements(), 1, encoder_out_dim)
+        # fmt: off
+        current_encoder_out = torch.index_select(
+            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1)
+        )
+        # fmt: on
+        logits = model.joiner(
+            current_encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
+        )
+        logits = logits.squeeze(1).squeeze(1)
+        log_probs = logits.log_softmax(dim=-1)
+        decoding_streams.advance(log_probs)
+    decoding_streams.terminate_and_flush_to_streams()
+    lattice = decoding_streams.format_output(encoder_out_lens.tolist())
+
+    best_path = one_best_decoding(lattice)
+    hyps = get_texts(best_path)
+    return hyps
+
 
 def greedy_search(
     model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int
@@ -48,7 +129,7 @@ def greedy_search(
     device = model.device
 
     decoder_input = torch.tensor(
-        [blank_id] * context_size, device=device
+        [blank_id] * context_size, device=device, dtype=torch.int64
     ).reshape(1, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
@@ -103,8 +184,9 @@ class Hypothesis:
     # Newly predicted tokens are appended to `ys`.
     ys: List[int]
 
-    # The log prob of ys
+    # The log prob of ys.
-    log_prob: float
+    # It contains only one entry.
+    log_prob: torch.Tensor
 
     @property
     def key(self) -> str:
@@ -113,7 +195,7 @@ class Hypothesis:
 
 
 class HypothesisList(object):
-    def __init__(self, data: Optional[Dict[str, Hypothesis]] = None):
+    def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None:
         """
         Args:
           data:
@@ -125,10 +207,10 @@ class HypothesisList(object):
             self._data = data
 
     @property
-    def data(self):
+    def data(self) -> Dict[str, Hypothesis]:
         return self._data
 
-    def add(self, hyp: Hypothesis):
+    def add(self, hyp: Hypothesis) -> None:
         """Add a Hypothesis to `self`.
 
         If `hyp` already exists in `self`, its probability is updated using
@@ -140,8 +222,10 @@ class HypothesisList(object):
         """
         key = hyp.key
         if key in self:
-            old_hyp = self._data[key]
+            old_hyp = self._data[key]  # shallow copy
-            old_hyp.log_prob = np.logaddexp(old_hyp.log_prob, hyp.log_prob)
+            torch.logaddexp(
+                old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob
+            )
         else:
             self._data[key] = hyp
 
@@ -153,7 +237,8 @@ class HypothesisList(object):
           length_norm:
             If True, the `log_prob` of a hypothesis is normalized by the
             number of tokens in it.
-
+        Returns:
+          Return the hypothesis that has the largest `log_prob`.
         """
         if length_norm:
             return max(
@@ -165,6 +250,9 @@ class HypothesisList(object):
     def remove(self, hyp: Hypothesis) -> None:
         """Remove a given hypothesis.
 
+        Caution:
+          `self` is modified **in-place**.
+
         Args:
           hyp:
             The hypothesis to be removed from `self`.
@@ -175,7 +263,7 @@ class HypothesisList(object):
         assert key in self, f"{key} does not exist"
         del self._data[key]
 
-    def filter(self, threshold: float) -> "HypothesisList":
+    def filter(self, threshold: torch.Tensor) -> "HypothesisList":
         """Remove all Hypotheses whose log_prob is less than threshold.
 
         Caution:
@@ -183,10 +271,10 @@ class HypothesisList(object):
 
         Returns:
           Return a new HypothesisList containing all hypotheses from `self`
-          that have `log_prob` being greater than the given `threshold`.
+          with `log_prob` being greater than the given `threshold`.
         """
         ans = HypothesisList()
-        for key, hyp in self._data.items():
+        for _, hyp in self._data.items():
             if hyp.log_prob > threshold:
                 ans.add(hyp)  # shallow copy
         return ans
@@ -216,6 +304,106 @@ class HypothesisList(object):
         return ", ".join(s)
 
 
+def modified_beam_search(
+    model: Transducer,
+    encoder_out: torch.Tensor,
+    beam: int = 4,
+) -> List[int]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
+      beam:
+        Beam size.
+    Returns:
+      Return the decoded result.
+    """
+
+    assert encoder_out.ndim == 3
+
+    # support only batch_size == 1 for now
+    assert encoder_out.size(0) == 1, encoder_out.size(0)
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+
+    device = model.device
+
+    T = encoder_out.size(1)
+
+    B = HypothesisList()
+    B.add(
+        Hypothesis(
+            ys=[blank_id] * context_size,
+            log_prob=torch.zeros(1, dtype=torch.float32, device=device),
+        )
+    )
+
+    for t in range(T):
+        # fmt: off
+        current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
+        # current_encoder_out is of shape (1, 1, 1, encoder_out_dim)
+        # fmt: on
+        A = list(B)
+        B = HypothesisList()
+
+        ys_log_probs = torch.cat([hyp.log_prob.reshape(1, 1) for hyp in A])
+        # ys_log_probs is of shape (num_hyps, 1)
+
+        decoder_input = torch.tensor(
+            [hyp.ys[-context_size:] for hyp in A],
+            device=device,
+            dtype=torch.int64,
+        )
+        # decoder_input is of shape (num_hyps, context_size)
+
+        decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
+        # decoder_output is of shape (num_hyps, 1, 1, decoder_output_dim)
+
+        current_encoder_out = current_encoder_out.expand(
+            decoder_out.size(0), 1, 1, -1
+        )  # (num_hyps, 1, 1, encoder_out_dim)
+
+        logits = model.joiner(
+            current_encoder_out,
+            decoder_out,
+        )
+        # logits is of shape (num_hyps, 1, 1, vocab_size)
+        logits = logits.squeeze(1).squeeze(1)
+
+        # now logits is of shape (num_hyps, vocab_size)
+        log_probs = logits.log_softmax(dim=-1)
+
+        log_probs.add_(ys_log_probs)
+
+        log_probs = log_probs.reshape(-1)
+        topk_log_probs, topk_indexes = log_probs.topk(beam)
+
+        # topk_hyp_indexes are indexes into `A`
+        topk_hyp_indexes = topk_indexes // logits.size(-1)
+        topk_token_indexes = topk_indexes % logits.size(-1)
+
+        topk_hyp_indexes = topk_hyp_indexes.tolist()
+        topk_token_indexes = topk_token_indexes.tolist()
+
+        for i in range(len(topk_hyp_indexes)):
+            hyp = A[topk_hyp_indexes[i]]
+            new_ys = hyp.ys[:]
+            new_token = topk_token_indexes[i]
+            if new_token != blank_id:
+                new_ys.append(new_token)
+            new_log_prob = topk_log_probs[i]
+            new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
+            B.add(new_hyp)
+
+    best_hyp = B.get_most_probable(length_norm=True)
+    ys = best_hyp.ys[context_size:]  # [context_size:] to remove blanks
+
+    return ys
+
+
 def beam_search(
     model: Transducer,
     encoder_out: torch.Tensor,
@@ -246,7 +434,9 @@ def beam_search(
     device = model.device
 
     decoder_input = torch.tensor(
-        [blank_id] * context_size, device=device
+        [blank_id] * context_size,
+        device=device,
+        dtype=torch.int64,
     ).reshape(1, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
@@ -283,7 +473,9 @@ def beam_search(
 
             if cached_key not in decoder_cache:
                 decoder_input = torch.tensor(
-                    [y_star.ys[-context_size:]], device=device
+                    [y_star.ys[-context_size:]],
+                    device=device,
+                    dtype=torch.int64,
                 ).reshape(1, context_size)
 
                 decoder_out = model.decoder(decoder_input, need_pad=False)
@@ -309,7 +501,7 @@ def beam_search(
 
             # First, process the blank symbol
             skip_log_prob = log_prob[blank_id]
-            new_y_star_log_prob = y_star.log_prob + skip_log_prob.item()
+            new_y_star_log_prob = y_star.log_prob + skip_log_prob
 
             # ys[:] returns a copy of ys
             B.add(Hypothesis(ys=y_star.ys[:], log_prob=new_y_star_log_prob))

egs/aishell/ASR/pruned_transducer_stateless/decode.py

@@ -20,12 +20,18 @@ import argparse
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import torch
 import torch.nn as nn
 from asr_datamodule import AishellAsrDataModule
-from beam_search import beam_search, greedy_search
+from beam_search import (
+    beam_search,
+    fast_beam_search,
+    greedy_search,
+    modified_beam_search,
+)
 from conformer import Conformer
 from decoder import Decoder
 from joiner import Joiner
@@ -85,6 +91,8 @@ def get_parser():
         help="""Possible values are:
           - greedy_search
           - beam_search
+          - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -92,7 +100,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --decoding-method is beam_search",
+        help="""An interger indicating how many candidates we will keep for each
+        frame. Used only when --decoding-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 --decoding-method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
     )
 
     parser.add_argument(
@@ -102,12 +138,14 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
+
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
         default=3,
         help="Maximum number of symbols per frame",
     )
+
     parser.add_argument(
         "--export",
         type=str2bool,
@@ -192,6 +230,7 @@ def decode_one_batch(
     model: nn.Module,
     lexicon: Lexicon,
     batch: dict,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
@@ -208,12 +247,15 @@ def decode_one_batch(
         It's the return value of :func:`get_params`.
       model:
         The neural model.
+      lexicon:
+        It contains the token symbol table and the word symbol table.
       batch:
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
-      lexicon:
+      decoding_graph:
-        It contains the token symbol table and the word symbol table.
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -232,32 +274,62 @@ def decode_one_batch(
         x=feature, x_lens=feature_lens
     )
     hyps = []
-    batch_size = encoder_out.size(0)
 
-    for i in range(batch_size):
+    if params.decoding_method == "fast_beam_search":
-        # fmt: off
+        hyp_tokens = fast_beam_search(
-        encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            model=model,
-        # fmt: on
+            decoding_graph=decoding_graph,
-        if params.decoding_method == "greedy_search":
+            encoder_out=encoder_out,
-            hyp = greedy_search(
+            encoder_out_lens=encoder_out_lens,
-                model=model,
+            beam=params.beam,
-                encoder_out=encoder_out_i,
+            max_contexts=params.max_contexts,
-                max_sym_per_frame=params.max_sym_per_frame,
+            max_states=params.max_states,
-            )
+        )
-        elif params.decoding_method == "beam_search":
+        for hyp in hyp_tokens:
-            hyp = beam_search(
+            hyps.append([lexicon.token_table[i] for i in hyp])
-                model=model, encoder_out=encoder_out_i, beam=params.beam_size
+    else:
-            )
+        batch_size = encoder_out.size(0)
-        else:
+
-            raise ValueError(
+        for i in range(batch_size):
-                f"Unsupported decoding method: {params.decoding_method}"
+            # fmt: off
-            )
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
-        hyps.append([lexicon.token_table[i] for i in hyp])
+            # 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,
+                )
+            elif params.decoding_method == "modified_beam_search":
+                hyp = modified_beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append([lexicon.token_table[i] for i in hyp])
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
     else:
-        return {f"beam_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
@@ -265,6 +337,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     lexicon: Lexicon,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -275,6 +348,11 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
+      lexicon:
+        It contains the token symbol table and the word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -303,6 +381,7 @@ def decode_dataset(
             model=model,
             lexicon=lexicon,
             batch=batch,
+            decoding_graph=decoding_graph,
         )
 
         for name, hyps in hyps_dict.items():
@@ -383,11 +462,21 @@ def main():
     params = get_params()
     params.update(vars(args))
 
-    assert params.decoding_method in ("greedy_search", "beam_search")
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "modified_beam_search",
+        "fast_beam_search",
+    )
     params.res_dir = params.exp_dir / params.decoding_method
 
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
-    if params.decoding_method == "beam_search":
+
+    if "fast_beam_search" in params.decoding_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.decoding_method:
         params.suffix += f"-beam-{params.beam_size}"
     else:
         params.suffix += f"-context-{params.context_size}"
@@ -435,6 +524,11 @@ def main():
     model.eval()
     model.device = device
 
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
@@ -451,6 +545,7 @@ def main():
             params=params,
             model=model,
             lexicon=lexicon,
+            decoding_graph=decoding_graph,
         )
 
         save_results(

egs/aishell/ASR/pruned_transducer_stateless/decoder.py

@@ -58,6 +58,7 @@ class Decoder(nn.Module):
             padding_idx=blank_id,
         )
         self.blank_id = blank_id
+        self.vocab_size = vocab_size
 
         assert context_size >= 1, context_size
         self.context_size = context_size