evaluation script

telugu_eval.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+import torch    
+import re
+import argparse
+
+from datasets import load_dataset, load_metric, Audio, Dataset
+from transformers import pipeline, AutoFeatureExtractor, Wav2Vec2ProcessorWithLM, Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC, AutoModelForCTC, AutoProcessor
+from typing import Dict
+
+
+def log_results(result: Dataset, args: Dict[str, str]):
+
+    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}"
+    )
+
+    with open(f"{dataset_id}_eval_results.txt", "w") as f:
+        f.write(result_str)
+
+    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 remove_special_characters(batch):
+    chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\&\/\d\_\\\]'
+    batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower()
+    batch["sentence"] = re.sub('\u200c', '', batch["sentence"])
+    batch["sentence"] = re.sub('[a-z]', '', batch["sentence"])
+    return batch
+
+def main(args):
+    # load dataset
+    dataset = load_dataset(args.dataset, args.config)
+    train_testvalid = dataset[args.split].train_test_split(test_size=0.25)
+    dataset_train = train_testvalid["train"]
+    dataset_test = train_testvalid["test"]
+
+    # load processor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
+    sampling_rate = feature_extractor.sampling_rate
+    print(sampling_rate)
+    dataset = dataset_test.map(remove_special_characters)
+    # resample audio
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
+
+    processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
+    model = Wav2Vec2ForCTC.from_pretrained(args.model_id) 
+    # processor = AutoProcessor.from_pretrained(args.model_id)
+    # model = AutoModelForCTC.from_pretrained(args.model_id) 
+    model.to("cuda")
+    # 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"] = batch["sentence"]
+    #     return batch
+
+    def evaluate(batch):
+        inputs = processor(batch["audio"]["array"], return_tensors="pt",sampling_rate=sampling_rate, padding=True)
+        with torch.no_grad():
+            logits = model(inputs.input_values.to("cuda")).logits
+        # pred_ids = torch.argmax(logits, dim=-1)
+        # batch["prediction"] = processor.batch_decode(pred_ids)
+        batch["prediction"] = processor.batch_decode(logits.cpu().numpy()).text
+        batch["target"] =batch["sentence"]
+
+        return batch
+
+    result = dataset.map(evaluate, remove_columns=dataset.column_names)
+
+    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)