Update eval.py

eval.py

@@ -5,10 +5,14 @@ from typing import Dict
 
 import torch
 from datasets import Audio, Dataset, load_dataset, load_metric
+from num2words import num2words as n2w
+from slugify import slugify
 
 from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
 # from pyctcdecode import BeamSearchDecoderCTC
 
+from cardinal_numbers import convert_nums
+
 
 def log_results(result: Dataset, args: Dict[str, str]):
     """DO NOT CHANGE. This function computes and logs the result metrics."""
@@ -16,7 +20,11 @@ def log_results(result: Dataset, args: Dict[str, str]):
     log_outputs = args.log_outputs
     lm = "withLM" if args.use_lm else "noLM"
     model_id = args.model_id.replace("/", "_").replace(".", "")
-    dataset_id = "_".join([model_id] + args.dataset.split("/") + [args.config, args.split, lm])
+    if args.filter:
+        extra_args = [args.config, slugify(args.filter), args.split, lm]
+    else:
+        extra_args = [args.config, args.split, lm]
+    dataset_id = "_".join([model_id] + args.dataset.split("/") + extra_args)
 
     # load metric
     wer = load_metric("wer")
@@ -32,6 +40,8 @@ def log_results(result: Dataset, args: Dict[str, str]):
 
     with open(f"{dataset_id}_eval_results.txt", "w") as f:
         f.write(result_str)
+    with open(f"{dataset_id}_eval_results.tsv", "w") as f:
+        f.write("\t".join([args.model_id, args.dataset, args.config, args.filter, args.split, str(lm), str(wer_result), str(cer_result)]))
 
     # log all results in text file. Possibly interesting for analysis
     if log_outputs is not None:
@@ -49,11 +59,42 @@ def log_results(result: Dataset, args: Dict[str, str]):
             result.map(write_to_file, with_indices=True)
 
 
-def normalize_text(text: str, dataset: str) -> str:
+def normalize_text(original_text: str, dataset: str) -> str:
     """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
 
+    text = original_text.lower()
+    if dataset.lower().endswith("fleurs"): 
+        replacements = (
+            (r"\be\.kr", "etter kristus fødsel"),
+            (r"\bf\.kr", "før kristi fødsel"),
+            (r"\bca[.]?\b", "circa"),
+            (r"(\d)\s*km/t", r"\1 kilometer i timen"),
+            (r"(\d)\s*km", r"\1 kilometer"),
+            (r"(\d)\s*cm", r"\1 centimeter"),
+            (r"(\d)\s*mm", r"\1 millimeter"),
+            (r"kl\.", "klokka"),
+            (r"f\.eks", "for eksempel"),
+        )
+        for abrev, expasion in replacements:
+            text = re.sub(abrev, expasion, text)
+        text = re.sub(r'(\d+)[-–](\d+)', r'\1 til \2', text)  # 1-89, 70-90
+        text = re.sub(r'(\d{2}):00', r'\1', text)  # 21:00
+        text = re.sub(r"(\d{2}):0(\d{1})", r"\1 null \2", text)  # 17:03
+        text = re.sub(r"(\d{1,2}):(\d{1,2})", r"\1 \2", text)  # 17:23 (time), 4:3 (aspect ratios)
+        text = re.sub(r"(1[1-9])00", r"\1 hundre", text)  # 1800, 1900
+        text = re.sub(r"(1[1-9])0([1-9])", r"\1 null \2 ", text)  # 1901, 1909
+        text = re.sub(r"(1[1-9])([1-9]\d)", r"\1 \2 ", text)  # 1911, 1987
+        text = re.sub(r"(20)0([1-9])", r"\1 null \2 ", text)  # 2009
+        text = re.sub(r"(20)(\d{2})", r"\1 \2 ", text)  # 2009
+        text = re.sub(r"(\d{1,3})[.](\d{1,2})", r"\1 dot \2 ", text)  # 802.11n, 2.5ghz (in English)
+        text = re.sub(r"(\d{1,2})[ .](\d{3})", r"\1\2", text)  # 10 000, 32.000
+        text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text)  # n-standard
+        # text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text.replace(".", ""))
+        text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: convert_nums(int(x.group(0)), nn=True), text.replace(".", ""))
+
+
     chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\–\_\\\+\#\/]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
-    text = re.sub(chars_to_ignore_regex, "", text.lower()) + " "
+    text = re.sub(chars_to_ignore_regex, "", text) + " "
 
     if dataset.lower().endswith("nst"):
         text = text.lower()
@@ -77,11 +118,23 @@ def normalize_text(text: str, dataset: str) -> str:
         text = re.sub('[ö]', 'ø', text)
         text = re.sub('[ç]', 'c', text)
         text = re.sub('[úùüû]', 'u', text)
-        text = re.sub('\s', ' ', text)
-    text = re.sub("<ee(eh)?>", "ĥ", text)
-    text = re.sub("<mmm?>", "ĥ", text)
-    text = re.sub("<qq>", "ĥ", text)
-    text = re.sub("<inaudible>", "ĥ", text)
+        text = re.sub('\s+', ' ', text)
+    elif dataset.lower().endswith("fleurs"): 
+        text = re.sub('[áàâ]', 'a', text)
+        text = re.sub('[ä]', 'æ', text)
+        text = re.sub('[éèëê]', 'e', text)
+        text = re.sub('[íìïî]', 'i', text)
+        text = re.sub('[óòöô]', 'o', text)
+        text = re.sub('[ö]', 'ø', text)
+        text = re.sub('[ç]', 'c', text)
+        text = re.sub('[úùüû]', 'u', text)
+        text = re.sub('[«»]', '', text)
+        text = re.sub('\s+', ' ', text)
+    text = re.sub('<e+h?>', 'ĥ', text)
+    text = re.sub('<m+>', 'ĥ', text)
+    text = re.sub('<q+>', 'ĥ', text)
+    text = re.sub('<inaudible>', 'ĥ', text)
+    text = re.sub('[<>]', '', text)
 
     # # In addition, we can normalize the target text, e.g. removing new lines characters etc...
     # # note that order is important here!
@@ -90,13 +143,18 @@ def normalize_text(text: str, dataset: str) -> str:
     # for t in token_sequences_to_ignore:
     #     text = " ".join(text.split(t))
 
-    return text
+    return text.strip()
 
 
 def main(args):
     # load dataset
     dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
-
+    if args.filter:
+        attribute, value = list(map(str.strip, args.filter.split(":")))
+        dataset = dataset.filter(
+            lambda x: x[attribute] == value,
+            desc=f"Filtering on {args.filter}",
+        )
     # for testing: only process the first two examples as a test
     # dataset = dataset.select(range(10))
 
@@ -141,7 +199,7 @@ def main(args):
         )
 
         batch["prediction"] = prediction["text"]
-        batch["target"] = normalize_text(batch["text"], args.dataset)
+        batch["target"] = normalize_text(batch[args.text_column], args.dataset)
         return batch
 
     # run inference on all examples
@@ -167,7 +225,13 @@ if __name__ == "__main__":
     parser.add_argument(
         "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
     )
+    parser.add_argument(
+        "--filter", type=str, default="", help="Simple filter on attributes. *E.g.* `region_of_youth:Troms` would pnly keep those samplesfor which the condition is met"
+    )
     parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
+    parser.add_argument(
+        "--text_column", type=str, default="text", help="Column name containing the transcription."
+    )
     parser.add_argument(
         "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
     )