File size: 7,294 Bytes
12f2205
 
 
 
 
 
 
0a9ff22
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12f2205
0a9ff22
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12f2205
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0a9ff22
 
 
 
 
12f2205
 
 
 
0a9ff22
 
 
 
 
 
 
 
12f2205
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
#!/usr/bin/env python3
import argparse
import re
from typing import Dict

from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline, AutomaticSpeechRecognitionPipeline

from transformers import Wav2Vec2CTCTokenizer

class Wav2Vec2WordpieceTokenizer(Wav2Vec2CTCTokenizer):
    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        word_delimiter_token="|",
        do_lower_case=False,
        **kwargs
    ):
        super().__init__(
            vocab_file=vocab_file,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            do_lower_case=do_lower_case,
            word_delimiter_token=word_delimiter_token,
            **kwargs,
        )

        self._create_trie(self.all_special_tokens_extended)
        
    def _tokenize(self, text, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer.
        """
        special_cases = set(['gia', 'qui', 'quy', 'que', 'qua'])
        output_tokens = []
        for token_idx, token in enumerate(text.split()):
            if token in special_cases:
                sub_tokens = [token[:2], token[2:]]
            else:
                end = len(token)
                sub_tokens = []
                while end > 0:
                    start = 0
                    cur_substr = None
                    while start < end:
                        substr = token[start:end]
                        if substr in self.encoder:
                            cur_substr = substr
                            break
                        start += 1
                    if cur_substr is None:
                        sub_tokens.insert(0, self.unk_token)
                        end = start - 1
                    else:
                        sub_tokens.insert(0, cur_substr)
                        end = start
            
            if token_idx > 0:
                output_tokens.append(self.word_delimiter_token)
            output_tokens.extend(sub_tokens)
        return output_tokens
    
    def decode_ids(
        self, 
        token_ids, 
        skip_special_tokens = False, 
        clean_up_tokenization_spaces = True,
        group_tokens: bool = True,
        spaces_between_special_tokens: bool = False,
    ) -> str:
        # For compatible with speechbrain interfaces
        return self.decode(
            token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            group_tokens=group_tokens,
            spaces_between_special_tokens=spaces_between_special_tokens
        )
        
def log_results(result: Dataset, args: Dict[str, str]):
    """DO NOT CHANGE. This function computes and logs the result metrics."""

    log_outputs = args.log_outputs
    dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])

    # load metric
    wer = load_metric("wer")
    cer = load_metric("cer")

    # compute metrics
    wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
    cer_result = cer.compute(references=result["target"], predictions=result["prediction"])

    # print & log results
    result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
    print(result_str)

    with open(f"{dataset_id}_eval_results.txt", "w") as f:
        f.write(result_str)

    # log all results in text file. Possibly interesting for analysis
    if log_outputs is not None:
        pred_file = f"log_{dataset_id}_predictions.txt"
        target_file = f"log_{dataset_id}_targets.txt"

        with open(pred_file, "w") as p, open(target_file, "w") as t:

            # mapping function to write output
            def write_to_file(batch, i):
                p.write(f"{i}" + "\n")
                p.write(batch["prediction"] + "\n")
                t.write(f"{i}" + "\n")
                t.write(batch["target"] + "\n")

            result.map(write_to_file, with_indices=True)


def normalize_text(text: str) -> str:
    """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""

    chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–|]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

    text = re.sub(chars_to_ignore_regex, "", text.lower())

    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
    # note that order is important here!
    token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]

    for t in token_sequences_to_ignore:
        text = " ".join(text.split(t))

    return text


def main(args):
    # load dataset
    dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)

    # for testing: only process the first two examples as a test
    dataset = dataset.select(range(10))

    # load processor
    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
    sampling_rate = feature_extractor.sampling_rate

    # load tokenizer
    tokenizer = Wav2Vec2WordpieceTokenizer(
        vocab_file = args.model_id + 'vocab.json',
    )

    # resample audio
    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))

    # load eval pipeline
    asr = pipeline(
      "automatic-speech-recognition", 
      model=args.model_id,
      tokenizer = tokenizer
      )
    # asr = AutomaticSpeechRecognitionPipeline(

    # )
    # map function to decode audio
    def map_to_pred(batch):
        prediction = asr(
            batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
        )

        batch["prediction"] = prediction["text"]
        batch["target"] = normalize_text(batch["sentence"])
        return batch

    # run inference on all examples
    result = dataset.map(map_to_pred, remove_columns=dataset.column_names)

    # compute and log_results
    # do not change function below
    log_results(result, args)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
    )
    parser.add_argument(
        "--dataset",
        type=str,
        required=True,
        help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
    )
    parser.add_argument(
        "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
    )
    parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
    parser.add_argument(
        "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
    )
    parser.add_argument(
        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
    )
    parser.add_argument(
        "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
    )
    args = parser.parse_args()

    main(args)