Create eval.py

eval.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+import argparse
+import re
+from typing import Dict
+from sklearn import feature_extraction
+
+import torch
+from src.data.normalization import normalize_string
+from datasets import Audio, Dataset, load_dataset, load_metric
+
+from transformers import (
+    AutoFeatureExtractor,
+    pipeline,
+    AutoTokenizer,
+    Wav2Vec2Processor,
+    Wav2Vec2ProcessorWithLM,
+    Wav2Vec2ForCTC,
+    AutoConfig,
+)
+
+
+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 = normalize_string(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
+    # feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
+    # sampling_rate = feature_extractor.sampling_rate
+
+    if args.ctcdecode:
+        processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
+        decoder = processor.decoder
+    else:
+        processor = Wav2Vec2Processor.from_pretrained(args.model_id)
+        decoder = None
+
+    feature_extractor = processor.feature_extractor
+    tokenizer = processor.tokenizer
+    sampling_rate = feature_extractor.sampling_rate
+
+    config = AutoConfig.from_pretrained(args.model_id)
+    model = Wav2Vec2ForCTC.from_pretrained(args.model_id)
+
+    # resample audio
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
+
+    # load eval pipeline
+    if args.device is None:
+        args.device = 0 if torch.cuda.is_available() else -1
+
+    asr = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        config=config,
+        feature_extractor=feature_extractor,
+        decoder=decoder,
+        tokenizer=tokenizer,
+        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,
+            #decoder_kwargs={"beam_width": args.beam_width},
+        )
+
+        batch["prediction"] = prediction["text"]
+        batch["target"] = normalize_text(
+            batch["sentence"], invalid_chars_regex, normalize_to_lower
+        )
+        return batch
+
+    def map_and_decode(batch):
+        inputs = processor(
+            batch["audio"]["array"],
+            sampling_rate=batch["audio"]["sampling_rate"],
+            return_tensors="pt",
+        )
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        transcription = processor.batch_decode(logits.numpy()).text
+        batch["prediction"] = transcription
+        batch["target"] = normalize_text(
+            batch["sentence"], invalid_chars_regex, normalize_to_lower
+        )
+        return batch
+
+    #         transcription = .lower()
+    # 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.",
+    )
+    parser.add_argument(
+        "--ctcdecode",
+        action="store_true",
+        help="Apply the ctc decoder to the output (only if present in the model card).",
+    )
+    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.",
+    )
+    parser.add_argument(
+        "--beam_width",
+        type=int,
+        default=1,
+        help="Beam width used by the pyctc decoder.",
+    )
+    args = parser.parse_args()
+
+    main(args)