add language model

alphabet.json

@@ -0,0 +1 @@
+{"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", "\u00e4", "\u00e5", "\u00e9", "\u00f4", "\u00f6", "\u00fc", "\u2047", "", "<s>", "</s>"], "is_bpe": false}

eval.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+from datasets import load_dataset, load_metric, Audio, Dataset
+from transformers import pipeline, AutoFeatureExtractor
+import re
+import argparse
+import unicodedata
+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) -> str:
+    """ DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. """
+
+    chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
+
+    text = text.lower()
+    # normalize non-standard (stylized) unicode characters
+    text = unicodedata.normalize('NFKC', text)
+    # remove punctuation
+    text = re.sub(chars_to_ignore_regex, "", text)
+
+    # Let's also make sure we split on all kinds of newlines, spaces, etc...
+    text = " ".join(text.split())
+
+    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
+    asr = pipeline("automatic-speech-recognition", model=args.model_id)
+
+    # map function to decode audio
+    def map_to_pred(batch):
+        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"])
+        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."
+    )
+    args = parser.parse_args()
+
+    main(args)

language_model/5gram.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c803936922612f71cf0abdb37763c18d24624e36bfa4abac20187cc17b88541d
+size 1981380707

language_model/attrs.json

@@ -0,0 +1 @@
+{"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}

preprocessor_config.json

@@ -4,6 +4,7 @@
   "feature_size": 1,
   "padding_side": "right",
   "padding_value": 0,
+  "processor_class": "Wav2Vec2ProcessorWithLM",
   "return_attention_mask": true,
   "sampling_rate": 16000
 }

special_tokens_map.json

@@ -1 +1 @@
-{"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}]}
+{"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}]}

tokenizer_config.json

@@ -1 +1 @@
-{"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"}
+{"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": "hf-test/xls-r-300m-sv", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}

wandb/run-20220127_121247-o8za41vn/files/config.yaml

@@ -4832,6 +4832,16 @@ _wandb:
       - 5
       - 11
       - 12
+      2:
+      - 1
+      - 2
+      - 3
+      - 5
+      - 11
+      - 12
+      3:
+      - 1
+      - 7
       4: 3.8.12
       5: 0.12.1
       6: 4.16.0.dev0

wandb/run-20220127_121247-o8za41vn/files/output.log

@@ -14028,3 +14028,19 @@ Upload file runs/Jan27_12-08-15_brutasse/events.out.tfevents.1643285566.brutasse
 Upload file runs/Jan27_12-08-15_brutasse/events.out.tfevents.1643285566.brutasse.15970.0:  73%|███████████████████████████████████████████████▉                  | 32.0k/44.1k [00:00<?, ?B/s]
 01/27/2022 20:35:38 - WARNING - huggingface_hub.repository - To https://huggingface.co/hf-test/xls-r-300m-sv-cv8
 Upload file wandb/run-20220127_121247-o8za41vn/run-o8za41vn.wandb: 100%|███████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+01/27/2022 20:36:03 - WARNING - huggingface_hub.repository - To https://huggingface.co/hf-test/xls-r-300m-sv-cv8
+   2b65b1d..753fa5f  main -> main
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████���███████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]
+Dropping the following result as it does not have all the necessary fields:████████████████████████████████████████████████████████████████████████████████| 118M/118M [04:02<00:00, 47.9kB/s]

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wandb/run-20220127_121247-o8za41vn/logs/debug.log

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