eval

README.md

@@ -1,12 +1,32 @@
 ---
 language:
 - fr
+license: apache-2.0
 tags:
 - automatic-speech-recognition
 - mozilla-foundation/common_voice_8_0
+- fr
 - robust-speech-event
 datasets:
 - mozilla-foundation/common_voice_8_0
-metrics:
-- wer
----
+model-index:
+- name: XLS-R-1B - French
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8
+      type: mozilla-foundation/common_voice_8_0
+      args: fr
+    metrics:
+       - name: Test WER (without LM)
+         type: wer
+         value: 15.405483405483406
+       - name: Test CER (without LM)
+         type: cer
+         value: 4.8773030225289137
+       - name: Test WER (with LM)
+         type: wer
+         value: 12.5
+---

eval.py

@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+from datasets import load_dataset, load_metric, Audio, Dataset
+from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer
+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:
+    # remove special characters
+    chars_to_ignore_regex = '[\µ\я\ひ\ⱎ\ⱅ\ḥ\ӌ\џ\ŵ\ʋ\λ\φ\χ\г\и\к\п\ц\ч\э\я\џ\ӌ\ቀ\ከ\ጀ\ḥ\牡\津\宇\厳\保\丹\三\む\も\ⱎ\ⱅ\⋅\⊨\↔\ℚ\э\п\к\и\г\|\£\§\·\½\º\ə\ơ\ǀ\ː\ʾ\ˢ\г\и\к\э\п\∨\„\,\?\.\!\—\―\–\;\:\"\‘\»\%\ł\_\€\×\ぬ\;\±\ß\Þ\«\Ø\°\…\”\“\`\ʿ\&\=\+\の\~\(\)\Σ\ı\ጠ\ℵ\馆\青\貴\西\美\甌\杜\术\星\文\扬\北\京\乃\ゔ\や\め\ま\へ\つ\た\う\い\☉\≥\®\/\∞\∆\∅\→\ℰ\ω\ψ\Μ\Θ\Κ\Π\Σ\Ω\α\γ\δ\ε\ζ\η\κ\ι\ν\μ\ρ\ς\σ\τ\υ\ℤ\ℝ\ℂ\ℕ\₽\∈\›\ο\‹\†\}\{\}\_\ደ\Δ\ወ\ي\و\ب\ة\د\ن\ن\ل\را\э\р\п\н\м\к\и\з\ψ\υ\θ\ṭ\ṯ\ḍ\*\^\∼\م\э\п\ǃ\$\Ꝑ]'
+    chars_to_replace_a = '[\ɑ\ạ\ả\ầ\ậ\ắ\ẵ\а\ǎ\ā\ă\ą\á\ã\ä\å]'
+    chars_to_replace_i = '[\ɨ\ị\ı\ī\ĩ\í\ì\і]'
+    chars_to_replace_e = '[\ệ\ễ\ề\ě\ę\ė\ē\е\ế]'
+    chars_to_replace_o = '[\ồ\ộ\ờ\ợ\ő\ö\ŏ\ō\ø\õ\ó\ò\ð\ǫ\ό\ớ\ổ\ố]'
+    chars_to_replace_u = '[\ų\ʉ\ủ\ử\ù\ü\ư\ǔ\ů\ū\ũ\ú\ứ\ụ\ű\ŭ]'
+    chars_to_replace_c = '[\ς\ć\ċ\č\ҫ]'
+    chars_to_replace_y = '[\ÿ\ỳ\ÿ\ý]'
+    chars_to_replace_n = '[\ṇ\ṅ\ǹ\ħ\ñ\ň\ņ\ń]'
+    chars_to_replace_t = '[\ṭ\ț\ť\ţ]'
+    chars_to_replace_s = '[\ṣ\ș\š\ş\ś]'
+    chars_to_replace_q = '[\զ\գ\գ\զ]'
+    chars_to_replace_j = '[\ј]'
+    chars_to_replace_z = '[\ž\ż\ź\ẓ]'
+    chars_to_replace_r = '[\ř]'
+    chars_to_replace_l = '[\ł\ļ\ĺ]'
+    chars_to_replace_k = '[\ķ]'
+    chars_to_replace_g = '[\ġ\ğ]'
+    chars_to_replace_d = '[\đ\ď]'
+    chars_to_replace_b = '[\þ]'
+    chars_to_replace_p = '[\р]'
+    chars_to_replace_apostrophe = '[\´\′\ʼ\’\'\'\ʽ\ʻ\ʾ]'
+    chars_to_replace_tirets = '[\─\−\‐]'
+
+
+    text = re.sub(chars_to_ignore_regex, " ", text).lower() if to_lower
+    text = re.sub(chars_to_replace_a, "a", text)
+    text = re.sub(chars_to_replace_i, "i", text)
+    text = re.sub(chars_to_replace_e, "e", text)
+    text = re.sub(chars_to_replace_o, "o", text)
+    text = re.sub(chars_to_replace_u, "u", text)
+    text = re.sub(chars_to_replace_c, "c", text)
+    text = re.sub(chars_to_replace_y, "y", text)
+    text = re.sub(chars_to_replace_n, "n", text)
+    text = re.sub(chars_to_replace_t, "t", text)
+    text = re.sub(chars_to_replace_s, "s", text)
+    text = re.sub(chars_to_replace_q, "q", text)
+    text = re.sub(chars_to_replace_j, "j", text)
+    text = re.sub(chars_to_replace_z, "z", text)
+    text = re.sub(chars_to_replace_r, "r", text)
+    text = re.sub(chars_to_replace_l, "l", text)
+    text = re.sub(chars_to_replace_k, "k", text)
+    text = re.sub(chars_to_replace_g, "g", text)
+    text = re.sub(chars_to_replace_d, "d", text)
+    text = re.sub(chars_to_replace_b, "b", text)
+    text = re.sub(chars_to_replace_q, "q", text)
+    text = re.sub(chars_to_replace_p, "p", text)
+    text = re.sub(chars_to_replace_apostrophe, "'", text)
+    text = re.sub(chars_to_replace_tirets, "-", text)
+    text = re.sub("β", "beta", text)
+    text = re.sub("æ", "ae", text)
+    text = re.sub("œ", "oe", text)
+    text = re.sub("&", "et", text)
+    text = re.sub("π", "pi", text)
+    text = re.sub("ľ", "l'", text) 
+    text = re.sub(r"^\s+|\s+$", "", text)
+    text = re.sub(" +", " ", text)
+    text = re.sub("\n", " ", text) 
+    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
+
+    # 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=args.model_id, 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)
+
+    # 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(
+        "--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)