versae commited on
Commit
03b6c2d
1 Parent(s): cdd5a7b

Adding no lang model evals, and the 5gram lang model

Browse files
.gitattributes CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ language_model/unigrams.txt filter=lfs diff=lfs merge=lfs -text
NbAiLab_NPSC_16K_mp3_bokmaal_test_eval_results_no_lang_model.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
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+ WER: 0.08298132342894532
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+ CER: 0.028500279919396503
add_kenlm.py ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import argparse
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+ from transformers import AutoProcessor
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+ from transformers import Wav2Vec2ProcessorWithLM
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+ from pyctcdecode import build_ctcdecoder
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+
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+
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+ def main(args):
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+ processor = AutoProcessor.from_pretrained(args.model_name_or_path)
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+ vocab_dict = processor.tokenizer.get_vocab()
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+ sorted_vocab_dict = {
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+ k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])
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+ }
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+ decoder = build_ctcdecoder(
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+ labels=list(sorted_vocab_dict.keys()),
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+ kenlm_model_path=args.kenlm_model_path,
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+ )
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+ processor_with_lm = Wav2Vec2ProcessorWithLM(
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+ feature_extractor=processor.feature_extractor,
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+ tokenizer=processor.tokenizer,
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+ decoder=decoder,
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+ )
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+ processor_with_lm.save_pretrained(args.model_name_or_path)
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+ print(f"Run: ~/bin/build_binary language_model/*.arpa language_model/5gram.bin -T $(pwd) && rm language_model/*.arpa")
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+
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+ def parse_args():
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+ parser = argparse.ArgumentParser()
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+ parser.add_argument('--model_name_or_path', default="./", help='Model name or path. Defaults to ./')
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+ parser.add_argument('--kenlm_model_path', required=True, help='Path to KenLM arpa file.')
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+ args = parser.parse_args()
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+ return args
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+
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+ if __name__ == "__main__":
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+ args = parse_args()
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+ main(args)
alphabet.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"labels": [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "\u00e5", "\u00e6", "\u00f8", "\u2047", "", "<s>", "</s>"], "is_bpe": false}
eval.py ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ #!/usr/bin/env python3
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+ import argparse
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+ import re
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+ from typing import Dict
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+
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+ import torch
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+ from datasets import Audio, Dataset, load_dataset, load_metric
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+
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+ from transformers import AutoFeatureExtractor, pipeline
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+
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+
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+ def log_results(result: Dataset, args: Dict[str, str]):
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+ """DO NOT CHANGE. This function computes and logs the result metrics."""
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+
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+ log_outputs = args.log_outputs
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+ dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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+
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+ # load metric
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+ wer = load_metric("wer")
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+ cer = load_metric("cer")
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+
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+ # compute metrics
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+ wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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+ cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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+
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+ # print & log results
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+ result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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+ print(result_str)
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+
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+ with open(f"{dataset_id}_eval_results.txt", "w") as f:
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+ f.write(result_str)
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+
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+ # log all results in text file. Possibly interesting for analysis
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+ if log_outputs is not None:
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+ pred_file = f"log_{dataset_id}_predictions.txt"
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+ target_file = f"log_{dataset_id}_targets.txt"
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+
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+ with open(pred_file, "w") as p, open(target_file, "w") as t:
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+
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+ # mapping function to write output
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+ def write_to_file(batch, i):
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+ p.write(f"{i}" + "\n")
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+ p.write(batch["prediction"] + "\n")
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+ t.write(f"{i}" + "\n")
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+ t.write(batch["target"] + "\n")
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+
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+ result.map(write_to_file, with_indices=True)
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+
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+
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+ def normalize_text(text: str) -> str:
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+ """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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+
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+ chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\–\_\\\+\#\/]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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+
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+ text = re.sub(chars_to_ignore_regex, "", text.lower()) + " "
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+ text = re.sub('[áàâ]', 'a', text)
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+ text = re.sub('[ä]', 'æ', text)
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+ text = re.sub('[éèëê]', 'e', text)
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+ text = re.sub('[íìïî]', 'i', text)
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+ text = re.sub('[óòöô]', 'o', text)
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+ text = re.sub('[ö]', 'ø', text)
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+ text = re.sub('[ç]', 'c', text)
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+ text = re.sub('[úùüû]', 'u', text)
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+ text = re.sub('\s', ' ', text)
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+ text = re.sub('<ee>', 'eee', text)
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+ text = re.sub('<qq>', 'qqq', text)
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+ text = re.sub('<mm>', 'mmm', text)
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+ text = re.sub('<inaudible>', 'xxx', text)
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+ text = re.sub('[<>]', '', text)
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+
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+ # # In addition, we can normalize the target text, e.g. removing new lines characters etc...
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+ # # note that order is important here!
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+ # token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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+
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+ # for t in token_sequences_to_ignore:
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+ # text = " ".join(text.split(t))
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+
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+ return text
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+
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+
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+ def main(args):
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+ # load dataset
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+ dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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+
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+ # for testing: only process the first two examples as a test
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+ # dataset = dataset.select(range(10))
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+
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+ # load processor
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+ feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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+ sampling_rate = feature_extractor.sampling_rate
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+
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+ # resample audio
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+ dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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+
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+ # load eval pipeline
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+ if args.device is None:
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+ args.device = 0 if torch.cuda.is_available() else -1
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+ asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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+
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+ # map function to decode audio
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+ def map_to_pred(batch):
102
+ prediction = asr(
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+ batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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+ )
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+
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+ batch["prediction"] = prediction["text"]
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+ batch["target"] = normalize_text(batch["text"])
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+ return batch
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+
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+ # run inference on all examples
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+ result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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+
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+ # compute and log_results
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+ # do not change function below
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+ log_results(result, args)
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+
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+
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+ if __name__ == "__main__":
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+ parser = argparse.ArgumentParser()
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+
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+ parser.add_argument(
122
+ "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
123
+ )
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+ parser.add_argument(
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+ "--dataset",
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+ type=str,
127
+ required=True,
128
+ help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
129
+ )
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+ parser.add_argument(
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+ "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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+ )
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+ parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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+ parser.add_argument(
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+ "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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+ )
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+ parser.add_argument(
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+ "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
139
+ )
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+ parser.add_argument(
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+ "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
142
+ )
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+ parser.add_argument(
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+ "--device",
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+ type=int,
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+ default=None,
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+ help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
148
+ )
149
+ args = parser.parse_args()
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+
151
+ main(args)
language_model/5gram.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7b41c24c63f2f0585bea83666369593f3b3e6d047f327a90f36ebca2c35ef0ff
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+ size 4243671427
language_model/attrs.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}
language_model/unigrams.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ac3e71ca49838ca355df6fdcb8d89344a5a9bf9e1a76587cdf5df1367c19b9a9
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+ size 16759269
log_NbAiLab_NPSC_16K_mp3_bokmaal_test_predictions_no_lang_model.txt ADDED
The diff for this file is too large to render. See raw diff
 
log_NbAiLab_NPSC_16K_mp3_bokmaal_test_targets_no_lang_model.txt ADDED
The diff for this file is too large to render. See raw diff
 
preprocessor_config.json CHANGED
@@ -4,6 +4,7 @@
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  "feature_size": 1,
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  "padding_side": "right",
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  "padding_value": 0,
 
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  "return_attention_mask": true,
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  "sampling_rate": 16000
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  }
 
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  "feature_size": 1,
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  "padding_side": "right",
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  "padding_value": 0,
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+ "processor_class": "Wav2Vec2ProcessorWithLM",
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  "return_attention_mask": true,
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  "sampling_rate": 16000
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  }
special_tokens_map.json CHANGED
@@ -1 +1 @@
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- {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
 
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+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
tokenizer_config.json CHANGED
@@ -1 +1 @@
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- {"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer"}
 
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+ {"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}