new eval script

README.md

@@ -140,6 +140,87 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
+The model can also be evaluated with in 10% chunks which needs less ressources (to be tested).
+
+```
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+import jiwer
+lang_id = "de"
+
+processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german")
+model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") 
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]'
+substitutions = {
+    'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]',
+    'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]',
+    'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]',
+    'c' : '[\č\ć\ç\с]',
+    'l' : '[\ł]',
+    'u' : '[\ú\ū\ứ\ů]',
+    'und' : '[\&]',
+    'r' : '[\ř]',
+    'y' : '[\ý]',
+    's' : '[\ś\š\ș\ş]',
+    'i' : '[\ī\ǐ\í\ï\î\ï]',
+    'z' : '[\ź\ž\ź\ż]',
+    'n' : '[\ñ\ń\ņ]',
+    'g' : '[\ğ]',
+    'ss' : '[\ß]',
+    't' : '[\ț\ť]',
+    'd' : '[\ď\đ]',
+    "'": '[\ʿ\་\’\`\´\ʻ\`\‘]',
+    'p': '\р'
+}
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+    for x in substitutions:
+        batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"])
+        speech_array, sampling_rate = torchaudio.load(batch["path"])
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
+
+
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def evaluate(batch):
+    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+    with torch.no_grad():
+        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
+
+H, S, D, I = 0, 0, 0, 0
+for i in range(10):
+    print("test["+str(10*i)+"%:"+str(10*(i+1))+"%]")
+    test_dataset = load_dataset("common_voice", "de", split="test["+str(10*i)+"%:"+str(10*(i+1))+"%]")
+    test_dataset = test_dataset.map(speech_file_to_array_fn)
+    result = test_dataset.map(evaluate, batched=True, batch_size=8)
+    predictions = result["pred_strings"]
+    targets = result["sentence"]
+    chunk_metrics = jiwer.compute_measures(targets, predictions)
+    H = H + chunk_metrics["hits"]
+    S = S + chunk_metrics["substitutions"]
+    D = D + chunk_metrics["deletions"]
+    I = I + chunk_metrics["insertions"]
+WER = float(S + D + I) / float(H + S + D)
+print("WER: {:2f}".format(WER*100).mean())
+```
+
 **Test Result**: 15.80 %