File size: 6,198 Bytes
c87916b
 
b8f626b
c87916b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b8f626b
 
 
 
 
 
 
 
 
c87916b
 
b8f626b
c87916b
 
 
 
b8f626b
 
 
 
 
 
 
 
c87916b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b8f626b
 
 
c87916b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b8f626b
 
 
c87916b
 
 
 
 
 
 
 
 
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
#!/usr/bin/env python3
from datasets import load_dataset, load_metric, Audio, Dataset
from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer, AutoConfig, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
import re
import torch
import argparse
from typing import Dict

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, invalid_chars_regex: str, to_lower: bool) -> str:
    """ DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. """

    text = text.lower() if to_lower else text.upper()

    text = re.sub(invalid_chars_regex, " ", text)

    text = re.sub("\s+", " ", text).strip()

    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
    if args.greedy:
        processor = Wav2Vec2Processor.from_pretrained(args.model_id)
        decoder = None
    else:
        processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
        decoder = processor.decoder

    feature_extractor = processor.feature_extractor
    tokenizer = processor.tokenizer

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

    # load eval pipeline
    if args.device is None:
        args.device = 0 if torch.cuda.is_available() else -1
    
    config = AutoConfig.from_pretrained(args.model_id)
    model = AutoModelForCTC.from_pretrained(args.model_id)
    
    #asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
    asr = pipeline("automatic-speech-recognition", config=config, model=model, tokenizer=tokenizer, 
                   feature_extractor=feature_extractor, decoder=decoder, device=args.device)
    
    # build normalizer config
    tokenizer = AutoTokenizer.from_pretrained(args.model_id)
    tokens = [x for x in tokenizer.convert_ids_to_tokens(range(0, tokenizer.vocab_size))]
    special_tokens = [
        tokenizer.pad_token, tokenizer.word_delimiter_token,
        tokenizer.unk_token, tokenizer.bos_token,
        tokenizer.eos_token,
    ]
    non_special_tokens = [x for x in tokens if x not in special_tokens]
    invalid_chars_regex = f"[^\s{re.escape(''.join(set(non_special_tokens)))}]"
    normalize_to_lower = False
    for token in non_special_tokens:
        if token.isalpha() and token.islower():
            normalize_to_lower = True
            break

    # map function to decode audio
    def map_to_pred(batch, args=args, asr=asr, invalid_chars_regex=invalid_chars_regex, normalize_to_lower=normalize_to_lower):
        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"], invalid_chars_regex, normalize_to_lower)
        return batch

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

    # filtering out empty targets
    result = result.filter(lambda example: example["target"] != "")

    # 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 None. For long audio files a good value would be 5.0 seconds."
    )
    parser.add_argument(
        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to None. For long audio files a good value would be 1.0 seconds."
    )
    parser.add_argument(
        "--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis."
    )
    parser.add_argument(
        "--greedy", action='store_true', help="If defined, the LM will be ignored during inference."
    )
    parser.add_argument(
        "--device",
        type=int,
        default=None,
        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
    )
    args = parser.parse_args()

    main(args)