update model card

README.md

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+---
+language: de
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: {XLSR Wav2Vec2 Large 53 CV-de}
+  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: 12.62
+---
+
+# Wav2Vec2-Large-XLSR-53-german #TODO: replace language with your {language}, *e.g.* French
+
+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) dataset.
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("common_voice", "de", split="test[:8]") # use a batch of 8 for demo purposes
+
+processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
+model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german") 
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+"""
+Preprocessing the dataset by:
+- loading audio files
+- resampling to 16kHz
+- converting to array
+- prepare input tensor using the processor
+"""
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    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)
+
+# run forward
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"])
+"""
+Example Result:
+
+Prediction: [
+    'zieh durch bittet draußen die schuhe aus',
+    'es kommt zugvorgebauten fo',
+    'ihre vorterstrecken erschienen it modemagazinen wie der voge karpes basar mariclair',
+    'fürliepert eine auch für manachen ungewöhnlich lange drittelliste',
+    'er wurde zu ehren des reichskanzlers otto von bismarck errichtet',
+    'was solls ich bin bereit',
+    'das internet besteht aus vielen computern die miteinander verbunden sind',
+    'der uranus ist der siebinteplanet in unserem sonnensystem s'
+]
+
+Reference: [
+    'Zieht euch bitte draußen die Schuhe aus.',
+    'Es kommt zum Showdown in Gstaad.',
+    'Ihre Fotostrecken erschienen in Modemagazinen wie der Vogue, Harper’s Bazaar und Marie Claire.',
+    'Felipe hat eine auch für Monarchen ungewöhnlich lange Titelliste.',
+    'Er wurde zu Ehren des Reichskanzlers Otto von Bismarck errichtet.',
+    'Was solls, ich bin bereit.',
+    'Das Internet besteht aus vielen Computern, die miteinander verbunden sind.',
+    'Der Uranus ist der siebente Planet in unserem Sonnensystem.'
+]
+"""
+```
+
+
+## Evaluation
+
+The model can be evaluated as follows on the German test data of Common Voice:
+
+
+```python
+import re
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+"""
+Evaluation on the full test set:
+- takes ~20mins (RTX 3090).
+- requires ~170GB RAM to compute the WER. A potential solution to this is computing it in chunks.
+  See https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5 on how to implement this.
+"""
+test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
+model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
+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()
+	speech_array, sampling_rate = torchaudio.load(batch["path"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	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("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
+
+result = test_dataset.map(evaluate, batched=True, batch_size=8) # batch_size=8 -> requires ~14.5GB GPU memory
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+# WER: 12.615308
+```
+
+**Test Result**: 12.62 %
+
+
+## Training
+
+The Common Voice German `train` and `validation` were used for training.
+The script used for training can be found [here](https://github.com/maxidl/wav2vec2).
+The model was trained for 50k steps, taking around 30 hours on a single A100.
+
+The arguments used for training this model are:
+```
+python run_finetuning.py \
+--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+--dataset_config_name="de" \
+--output_dir=./wav2vec2-large-xlsr-german \
+--preprocessing_num_workers="16" \
+--overwrite_output_dir \
+--num_train_epochs="20" \
+--per_device_train_batch_size="64" \
+--per_device_eval_batch_size="32" \
+--learning_rate="1e-4" \
+--warmup_steps="500" \
+--evaluation_strategy="steps" \
+--save_steps="5000" \
+--eval_steps="5000" \
+--logging_steps="1000" \
+--save_total_limit="3" \
+--freeze_feature_extractor \
+--activation_dropout="0.055" \
+--attention_dropout="0.094" \
+--feat_proj_dropout="0.04" \
+--layerdrop="0.04" \
+--mask_time_prob="0.08" \
+--gradient_checkpointing="1" \
+--fp16 \
+--do_train \
+--do_eval \
+--dataloader_num_workers="16" \
+--group_by_length
+```