add eval_kenlm.py

eval_kenlm.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+import argparse
+import re
+from typing import Dict
+
+import torch
+from datasets import Audio, Dataset, load_dataset, load_metric
+
+from transformers import AutoFeatureExtractor, pipeline, Wav2Vec2ForCTC, Wav2Vec2Processor, set_seed
+from pyctcdecode import build_ctcdecoder
+from multiprocessing import Pool
+
+class KenLM:
+    def __init__(self, tokenizer, model_name, unigrams=None, num_workers=8, beam_width=128):
+        self.num_workers = num_workers
+        self.beam_width = beam_width
+        vocab_dict = tokenizer.get_vocab()
+        self.vocabulary = [x[0] for x in sorted(vocab_dict.items(), key=lambda x: x[1], reverse=False)]
+        self.vocabulary = self.vocabulary[:-1]
+        self.decoder = build_ctcdecoder(self.vocabulary, model_name, unigrams=unigrams)
+
+    @staticmethod
+    def lm_postprocess(text):
+        return ' '.join([x if len(x) > 1 else "" for x in text.split()]).strip()
+
+    def decode(self, logits):
+        probs = logits.cpu().numpy()
+        # probs = logits.numpy()
+        with Pool(self.num_workers) as pool:
+            text = self.decoder.decode_batch(pool, probs)
+            text = [KenLM.lm_postprocess(x) for x in text]
+        return text
+
+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 = '[,?.!-;:""%\'"\'\'`…’»«‘“”�éû]'  # 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, data_dir=args.data_dir, 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
+
+    # resample audio
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
+
+    # load eval pipeline
+    if args.device is None:
+        args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    set_seed(42)  # set the random seed to have reproducible result.
+    processor = Wav2Vec2Processor.from_pretrained(args.model_id)
+    model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
+    kenlm = KenLM(processor.tokenizer, "language_model/5gram.bin", unigrams="language_model/unigrams.txt")
+
+    # map function to decode audio
+    def map_to_pred(batch):
+        inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt", padding=True)
+        with torch.no_grad():
+            logits = model(inputs.input_values.to(args.device), attention_mask=inputs.attention_mask.to(args.device)).logits
+        prediction = kenlm.decode(logits)
+
+        batch["prediction"] = prediction
+        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("--data_dir", type=str, required=False, default=None,
+                        help="The directory contains the dataset")
+    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."
+    )
+    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)