Create README.md

README.md

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+---
+language: de
+datasets:
+- common_voice
+metrics:
+- wer
+- cer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 German with LM by Florian Zimmermeister
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice de
+      type: common_voice
+      args: de
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 5.7467896819046755
+       - name: Test CER
+         type: cer
+         value: 1.8980142607670552
+---
+
+**Test Result**
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| flozi00/wav2vec2-large-xlsr-53-german-with-lm | **5.7467896819046755%** | **1.8980142607670552%** |
+
+## Evaluation
+The model can be evaluated as follows on the German test data of Common Voice.
+
+```python
+import torchaudio.functional as F
+import torch
+from transformers import AutoModelForCTC, AutoProcessor
+import re
+from datasets import load_dataset, load_metric
+
+CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
+                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
+                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
+                   "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
+                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
+
+chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
+
+counter = 0
+wer_counter = 0
+cer_counter = 0
+
+def main():
+    model = AutoModelForCTC.from_pretrained("wav2vec2-large-xlsr-53-german-with-lm")
+    processor = AutoProcessor.from_pretrained("wav2vec2-large-xlsr-53-german-with-lm")
+
+    wer = load_metric("wer")
+    cer = load_metric("cer")
+
+    ds = load_dataset("common_voice", "de", split="test")
+    #ds = ds.select(range(100))
+
+    def calculate_metrics(batch):
+        global counter, wer_counter, cer_counter
+        resampled_audio = F.resample(torch.tensor(batch["audio"]["array"]), 48_000, 16_000).numpy()
+
+        input_values = processor(resampled_audio, return_tensors="pt", sampling_rate=16_000).input_values
+
+        with torch.no_grad():
+            logits = model(input_values).logits.numpy()[0]
+
+
+        decoded = processor.decode(logits)
+        pred = decoded.text
+
+        ref = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
+
+        wer_result = wer.compute(predictions=[pred], references=[ref])
+        cer_result = cer.compute(predictions=[pred], references=[ref])
+
+        counter += 1
+        wer_counter += wer_result
+        cer_counter += cer_result
+
+        print(f"WER: {(wer_counter/counter)*100} | CER: {(cer_counter/counter)*100}")
+
+        return batch
+
+
+    ds.map(calculate_metrics, remove_columns=ds.column_names)
+    
+main()
+```