File size: 5,306 Bytes
c8bff8b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import argparse
import pyarabic.araby as araby
from transformers import pipeline
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
from tqdm.auto import tqdm

wer_metric = evaluate.load("wer")


def is_target_text_in_range(ref):
    if ref.strip() == "ignore time segment in scoring":
        return False
    else:
        return ref.strip() != ""


def get_text(sample):
    if "text" in sample:
        return sample["text"]
    elif "sentence" in sample:
        return sample["sentence"]
    elif "normalized_text" in sample:
        return sample["normalized_text"]
    elif "transcript" in sample:
        return sample["transcript"]
    elif "transcription" in sample:
        return sample["transcription"]
    else:
        raise ValueError(
            f"Expected transcript column of either 'text', 'sentence', 'normalized_text' or 'transcript'. Got sample of "
            ".join{sample.keys()}. Ensure a text column name is present in the dataset."
        )


whisper_norm = BasicTextNormalizer()


def normalise(batch):
    batch["norm_text"] = get_text(batch)
    return batch


def remove_diacritics(batch):
    batch["norm_text"] = araby.strip_diacritics(get_text(batch))
    return batch


def data(dataset):
    for i, item in enumerate(dataset):
        yield {**item["audio"], "reference": item["norm_text"]}


def main(args):
    batch_size = args.batch_size
    whisper_asr = pipeline(
        "automatic-speech-recognition", model=args.model_id, device=args.device
    )

    whisper_asr.model.config.forced_decoder_ids = (
        whisper_asr.tokenizer.get_decoder_prompt_ids(
            language=args.language, task="transcribe"
        )
    )

    dataset = load_dataset(
        args.dataset,
        args.config,
        split=args.split,
        streaming=args.streaming,
        use_auth_token=True,
    )
    # Only uncomment for debugging
    if args.streaming:
        dataset = dataset.take(args.max_eval_samples)
    else:
        if args.max_eval_samples is not None:
            dataset = dataset.select(range(args.max_eval_samples))

    dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
    dataset = dataset.map(normalise)
    dataset = dataset.filter(is_target_text_in_range, input_columns=["norm_text"])

    predictions = []
    references = []

    # run streamed inference
    if not args.streaming:
        pbar = tqdm(total=len(dataset))

    for out in whisper_asr(data(dataset), batch_size=batch_size):

        pred = out["text"]
        true = out["reference"][0]

        if args.remove_diacritics:
            pred = araby.strip_diacritics(pred)
            true = araby.strip_diacritics(true)

        if args.normalise:
            pred = whisper_norm(pred)
            true = whisper_norm(true)

        predictions.append(pred)
        references.append(true)

        if not args.streaming:
            pbar.update(1)
    if not args.streaming:
        pbar.close()
    wer = wer_metric.compute(references=references, predictions=predictions)
    wer = round(100 * wer, 2)

    print("WER:", wer)


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,
        default="mozilla-foundation/common_voice_11_0",
        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 the English split of Common Voice",
    )
    parser.add_argument(
        "--split",
        type=str,
        default="test",
        help="Split of the dataset. *E.g.* `'test'`",
    )

    parser.add_argument(
        "--device",
        type=int,
        default=-1,
        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=16,
        help="Number of samples to go through each streamed batch.",
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help="Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.",
    )
    parser.add_argument(
        "--streaming",
        default=False,
        action="store_true",
        help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.",
    )
    parser.add_argument(
        "--language",
        type=str,
        required=True,
        help="Two letter language code for the transcription language, e.g. use 'en' for English.",
    )

    parser.add_argument(
        "--remove_diacritics",
        default=False,
        action="store_true",
        help="Choose whether you'd like remove_diacritics",
    )

    parser.add_argument(
        "--normalise",
        default=False,
        action="store_true",
        help="Choose whether you'd like whisper norm",
    )

    args = parser.parse_args()

    main(args)