File size: 3,359 Bytes
7a8d542 f471bbd 7a8d542 4d1abca 7a8d542 c70c30b 7a8d542 2c5a021 e714445 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
language: de
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 German with LM by Florian Zimmermeister @A\\Ware
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("flozi00/wav2vec2-large-xlsr-53-german-with-lm")
processor = AutoProcessor.from_pretrained("flozi00/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()
```
Credits:
The Acoustic model is an copy of [jonatasgrosman's model](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) I used to train an matching kenlm language model for |