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---
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language: de
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datasets:
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- common_voice
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metrics:
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- wer
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- cer
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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- xlsr-fine-tuning-week
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license: apache-2.0
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model-index:
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- name: XLSR Wav2Vec2 German by Jonatas Grosman
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  results:
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  - task: 
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      name: Speech Recognition
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      type: automatic-speech-recognition
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    dataset:
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      name: Common Voice de
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      type: common_voice
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      args: de
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    metrics:
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       - name: Test WER
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         type: wer
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         value: 10.55
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       - name: Test CER
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         type: cer
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         value: 2.81
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---
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# Wav2Vec2-Large-XLSR-53-German
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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).
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When using this model, make sure that your speech input is sampled at 16kHz.
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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/) :)
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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## Usage
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The model can be used directly (without a language model) as follows...
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Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
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```python
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from asrecognition import ASREngine
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asr = ASREngine("de", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-german")
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
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transcriptions = asr.transcribe(audio_paths)
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```
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Writing your own inference script:
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```python
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import torch
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import librosa
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "de"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-german"
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SAMPLES = 10
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test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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    batch["speech"] = speech_array
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    batch["sentence"] = batch["sentence"].upper()
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    return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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predicted_sentences = processor.batch_decode(predicted_ids)
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for i, predicted_sentence in enumerate(predicted_sentences):
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    print("-" * 100)
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    print("Reference:", test_dataset[i]["sentence"])
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    print("Prediction:", predicted_sentence)
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```
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| Reference  | Prediction |
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| ------------- | ------------- |
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| ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS. | ZIEHT EUCH BITTE DRAUSSEN DIE SCHUHE AUS |
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| ES KOMMT ZUM SHOWDOWN IN GSTAAD. | ES KOMMT ZUG STUNDEDAUTENESTERKT |
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| 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 |
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| FELIPE HAT EINE AUCH FÜR MONARCHEN UNGEWÖHNLICH LANGE TITELLISTE. | FELIPPE HAT EINE AUCH FÜR MONACHEN UNGEWÖHNLICH LANGE TITELLISTE |
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| ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET. | ER WURDE ZU EHREN DES REICHSKANZLERS OTTO VON BISMARCK ERRICHTET   M |
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| WAS SOLLS, ICH BIN BEREIT. | WAS SOLL'S ICH BIN BEREIT |
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| DAS INTERNET BESTEHT AUS VIELEN COMPUTERN, DIE MITEINANDER VERBUNDEN SIND. | DAS INTERNET BESTEHT AUS VIELEN COMPUTERN DIE MITEINANDER VERBUNDEN SIND |
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| DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM. | DER URANUS IST DER SIEBENTE PLANET IN UNSEREM SONNENSYSTEM |
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| DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND. | DIE WAGEN ERHIELTEN EIN EINHEITLICHES ERSCHEINUNGSBILD IN WEISS MIT ROTEM FENSTERBAND |
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| SIE WAR DIE COUSINE VON CARL MARIA VON WEBER. | SIE WAR DIE COUSINE VON KARL-MARIA VON WEBER |
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## Evaluation
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The model can be evaluated as follows on the German test data of Common Voice.
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```python
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import torch
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import re
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import librosa
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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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LANG_ID = "de"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-german"
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DEVICE = "cuda"
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
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                   "、", "﹂", "﹁", "‧", "~", "﹏", ",", "{", "}", "(", ")", "[", "]", "【", "】", "‥", "〽",
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                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", ":", "!", "?", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
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test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
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cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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model.to(DEVICE)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def speech_file_to_array_fn(batch):
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    with warnings.catch_warnings():
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        warnings.simplefilter("ignore")
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        speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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    batch["speech"] = speech_array
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    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
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    return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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# Preprocessing the datasets.
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# We need to read the audio files as arrays
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def evaluate(batch):
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    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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    with torch.no_grad():
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        logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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    pred_ids = torch.argmax(logits, dim=-1)
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    batch["pred_strings"] = processor.batch_decode(pred_ids)
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    return batch
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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predictions = [x.upper() for x in result["pred_strings"]]
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references = [x.upper() for x in result["sentence"]]
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print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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```
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**Test Result**:
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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.
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| jonatasgrosman/wav2vec2-large-xlsr-53-german | **10.55%** | **2.81%** |
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| Noricum/wav2vec2-large-xlsr-53-german | 11.06% | 2.99% |
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| maxidl/wav2vec2-large-xlsr-german | 13.10% | 3.64% |
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| marcel/wav2vec2-large-xlsr-53-german | 15.97% | 4.37% |
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| flozi00/wav2vec-xlsr-german | 16.13% | 4.33% |
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| facebook/wav2vec2-large-xlsr-53-german | 17.15% | 5.79% |
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| MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-German | 19.31% | 5.41% |
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