|
--- |
|
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 by Jonatas Grosman |
|
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: 10.55 |
|
- name: Test CER |
|
type: cer |
|
value: 2.81 |
|
--- |
|
|
|
# Wav2Vec2-Large-XLSR-53-German |
|
|
|
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice). |
|
When using this model, make sure that your speech input is sampled at 16kHz. |
|
|
|
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) |
|
|
|
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint |
|
|
|
## Usage |
|
|
|
The model can be used directly (without a language model) as follows: |
|
|
|
```python |
|
import torch |
|
import librosa |
|
from datasets import load_dataset |
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
|
|
|
LANG_ID = "de" |
|
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-german" |
|
SAMPLES = 10 |
|
|
|
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") |
|
|
|
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
|
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
|
|
|
# Preprocessing the datasets. |
|
# We need to read the audio files as arrays |
|
def speech_file_to_array_fn(batch): |
|
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
|
batch["speech"] = speech_array |
|
batch["sentence"] = batch["sentence"].upper() |
|
return batch |
|
|
|
test_dataset = test_dataset.map(speech_file_to_array_fn) |
|
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
|
|
|
with torch.no_grad(): |
|
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
|
|
|
predicted_ids = torch.argmax(logits, dim=-1) |
|
predicted_sentences = processor.batch_decode(predicted_ids) |
|
|
|
for i, predicted_sentence in enumerate(predicted_sentences): |
|
print("-" * 100) |
|
print("Reference:", test_dataset[i]["sentence"]) |
|
print("Prediction:", predicted_sentence) |
|
``` |
|
|
|
| Reference | Prediction | |
|
| ------------- | ------------- | |
|
| ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS. | ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS | |
|
| ES KOMMT ZUM SHOWDOWN IN GSTAAD. | ES KOMMT ZUG STUNDEDAUTENESTERKT | |
|
| IHRE FOTOSTRECKEN ERSCHIENEN IN MODEMAGAZINEN WIE DER VOGUE, HARPER’S BAZAAR UND MARIE CLAIRE. | IHRE FOTELSTRECKEN ERSCHIENEN MIT MODEMAGAZINEN WIE DER VALG AT DAS BASIN MA RIQUAIR | |
|
| FELIPE HAT EINE AUCH FÜR MONARCHEN UNGEWÖHNLICH LANGE TITELLISTE. | FELIPPE HAT EINE AUCH FÜR MONACHEN UNGEWÖHNLICH LANGE TITELLISTE | |
|
| ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET. | ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET M | |
|
| WAS SOLLS, ICH BIN BEREIT. | WAS SOLL'S ICH BIN BEREIT | |
|
| DAS INTERNET BESTEHT AUS VIELEN COMPUTERN, DIE MITEINANDER VERBUNDEN SIND. | DAS INTERNET BESTEHT AUS VIELEN COMPUTERN DIE MITEINANDER VERBUNDEN SIND | |
|
| DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM. | DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM | |
|
| DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND. | DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND | |
|
| SIE WAR DIE COUSINE VON CARL MARIA VON WEBER. | SIE WAR DIE COUSINE VON KARL-MARIA VON WEBER | |
|
|
|
## Evaluation |
|
|
|
The model can be evaluated as follows on the German test data of Common Voice. |
|
|
|
```python |
|
import torch |
|
import re |
|
import librosa |
|
from datasets import load_dataset, load_metric |
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
|
|
|
LANG_ID = "de" |
|
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-german" |
|
DEVICE = "cuda" |
|
|
|
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", |
|
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", |
|
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", |
|
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", |
|
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"] |
|
|
|
test_dataset = load_dataset("common_voice", LANG_ID, split="test") |
|
|
|
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py |
|
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py |
|
|
|
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" |
|
|
|
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
|
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
|
model.to(DEVICE) |
|
|
|
# Preprocessing the datasets. |
|
# We need to read the audio files as arrays |
|
def speech_file_to_array_fn(batch): |
|
with warnings.catch_warnings(): |
|
warnings.simplefilter("ignore") |
|
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
|
batch["speech"] = speech_array |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() |
|
return batch |
|
|
|
test_dataset = test_dataset.map(speech_file_to_array_fn) |
|
|
|
# Preprocessing the datasets. |
|
# We need to read the audio 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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits |
|
|
|
pred_ids = torch.argmax(logits, dim=-1) |
|
batch["pred_strings"] = processor.batch_decode(pred_ids) |
|
return batch |
|
|
|
result = test_dataset.map(evaluate, batched=True, batch_size=8) |
|
|
|
predictions = [x.upper() for x in result["pred_strings"]] |
|
references = [x.upper() for x in result["sentence"]] |
|
|
|
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
|
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
|
``` |
|
|
|
**Test Result**: |
|
|
|
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-06-17). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. |
|
|
|
| Model | WER | CER | |
|
| ------------- | ------------- | ------------- | |
|
| jonatasgrosman/wav2vec2-large-xlsr-53-german | **10.55%** | **2.81%** | |
|
| Noricum/wav2vec2-large-xlsr-53-german | 11.06% | 2.99% | |
|
| maxidl/wav2vec2-large-xlsr-german | 13.10% | 3.64% | |
|
| marcel/wav2vec2-large-xlsr-53-german | 15.97% | 4.37% | |
|
| flozi00/wav2vec-xlsr-german | 16.13% | 4.33% | |
|
| facebook/wav2vec2-large-xlsr-53-german | 17.15% | 5.79% | |
|
| MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-German | 19.31% | 5.41% | |
|
|
