decoder changes

1 files changed, 87 insertions, 34 deletions

diff --git a/swr2_asr/utils/decoder.py b/swr2_asr/utils/decoder.py
index 098f6a4..2b6d29b 100644
--- a/swr2_asr/utils/decoder.py
+++ b/swr2_asr/utils/decoder.py
@@ -1,4 +1,5 @@
 """Decoder for CTC-based ASR.""" ""
+from dataclasses import dataclass
 import os
 
 import torch
@@ -9,37 +10,39 @@ from swr2_asr.utils.data import create_lexicon
 from swr2_asr.utils.tokenizer import CharTokenizer
 
 
-# TODO: refactor to use torch CTC decoder class
-def greedy_decoder(
-    output, labels, label_lengths, tokenizer: CharTokenizer, collapse_repeated=True
-):  # pylint: disable=redefined-outer-name
-    """Greedily decode a sequence."""
-    blank_label = tokenizer.get_blank_token()
-    arg_maxes = torch.argmax(output, dim=2)  # pylint: disable=no-member
-    decodes = []
-    targets = []
-    for i, args in enumerate(arg_maxes):
-        decode = []
-        targets.append(tokenizer.decode(labels[i][: label_lengths[i]].tolist()))
-        for j, index in enumerate(args):
-            if index != blank_label:
-                if collapse_repeated and j != 0 and index == args[j - 1]:
-                    continue
-                decode.append(index.item())
-        decodes.append(tokenizer.decode(decode))
-    return decodes, targets
-
-
-def beam_search_decoder(
+@dataclass
+class DecoderOutput:
+    """Decoder output."""
+
+    words: list[str]
+
+
+def decoder_factory(decoder_type: str = "greedy") -> callable:
+    """Decoder factory."""
+    if decoder_type == "greedy":
+        return get_greedy_decoder
+    if decoder_type == "lm":
+        return get_beam_search_decoder
+    raise NotImplementedError
+
+
+def get_greedy_decoder(
+    tokenizer: CharTokenizer,  # pylint: disable=redefined-outer-name
+    *_,
+):
+    """Greedy decoder."""
+    return GreedyDecoder(tokenizer)
+
+
+def get_beam_search_decoder(
     tokenizer: CharTokenizer,  # pylint: disable=redefined-outer-name
-    tokens_path: str,
-    lang_model_path: str,
-    language: str,
     hparams: dict,  # pylint: disable=redefined-outer-name
 ):
     """Beam search decoder."""
-
-    n_gram, beam_size, beam_threshold, n_best, lm_weight, word_score = (
+    hparams = hparams.get("lm", {})
+    language, lang_model_path, n_gram, beam_size, beam_threshold, n_best, lm_weight, word_score = (
+        hparams["language"],
+        hparams["language_model_path"],
         hparams["n_gram"],
         hparams["beam_size"],
         hparams["beam_threshold"],
@@ -53,6 +56,7 @@ def beam_search_decoder(
         torch.hub.download_url_to_file(url, f"data/mls_lm_{language}.tar.gz")
         _extract_tar("data/mls_lm_{language}.tar.gz", overwrite=True)
 
+    tokens_path = os.path.join(lang_model_path, f"mls_lm_{language}", "tokens.txt")
     if not os.path.isfile(tokens_path):
         tokenizer.create_tokens_txt(tokens_path)
 
@@ -79,11 +83,66 @@ def beam_search_decoder(
     return decoder
 
 
+class GreedyDecoder:
+    """Greedy decoder."""
+
+    def __init__(self, tokenizer: CharTokenizer):  # pylint: disable=redefined-outer-name
+        self.tokenizer = tokenizer
+
+    def __call__(
+        self, output, greedy_type: str = "inference", labels=None, label_lengths=None
+    ):  # pylint: disable=redefined-outer-name
+        """Greedily decode a sequence."""
+        if greedy_type == "train":
+            res = self.train(output, labels, label_lengths)
+        if greedy_type == "inference":
+            res = self.inference(output)
+
+        res = [[DecoderOutput(words=res)]]
+        return res
+
+    def train(self, output, labels, label_lengths):
+        """Greedily decode a sequence with known labels."""
+        blank_label = tokenizer.get_blank_token()
+        arg_maxes = torch.argmax(output, dim=2)  # pylint: disable=no-member
+        decodes = []
+        targets = []
+        for i, args in enumerate(arg_maxes):
+            decode = []
+            targets.append(self.tokenizer.decode(labels[i][: label_lengths[i]].tolist()))
+            for j, index in enumerate(args):
+                if index != blank_label:
+                    if j != 0 and index == args[j - 1]:
+                        continue
+                    decode.append(index.item())
+            decodes.append(self.tokenizer.decode(decode))
+        return decodes, targets
+
+    def inference(self, output):
+        """Greedily decode a sequence."""
+        collapse_repeated = True
+        arg_maxes = torch.argmax(output, dim=2)  # pylint: disable=no-member
+        blank_label = self.tokenizer.get_blank_token()
+        decodes = []
+        for args in arg_maxes:
+            decode = []
+            for j, index in enumerate(args):
+                if index != blank_label:
+                    if collapse_repeated and j != 0 and index == args[j - 1]:
+                        continue
+                    decode.append(index.item())
+            decodes.append(self.tokenizer.decode(decode))
+
+        return decodes
+
+
 if __name__ == "__main__":
     tokenizer = CharTokenizer.from_file("data/tokenizers/char_tokenizer_german.json")
     tokenizer.create_tokens_txt("data/tokenizers/tokens_german.txt")
 
     hparams = {
+        "language": "german",
+        "lang_model_path": "data",
         "n_gram": 3,
         "beam_size": 100,
         "beam_threshold": 100,
@@ -92,10 +151,4 @@ if __name__ == "__main__":
         "word_score": 1.0,
     }
 
-    beam_search_decoder(
-        tokenizer,
-        "data/tokenizers/tokens_german.txt",
-        "data",
-        "german",
-        hparams,
-    )
+    get_beam_search_decoder(tokenizer, hparams)