219 lines
7.0 KiB
Markdown
219 lines
7.0 KiB
Markdown
---
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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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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 Large 53 CV-de}
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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: 12.77
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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) dataset.
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When using this model, make sure that your speech input is sampled at 16kHz.
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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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```python
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import torch
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import torchaudio
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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test_dataset = load_dataset("common_voice", "de", split="test[:8]") # use a batch of 8 for demo purposes
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processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
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model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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"""
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Preprocessing the dataset by:
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- loading audio files
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- resampling to 16kHz
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- converting to array
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- prepare input tensor using the processor
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"""
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def speech_file_to_array_fn(batch):
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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = resampler(speech_array).squeeze().numpy()
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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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# run forward
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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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print("Prediction:", processor.batch_decode(predicted_ids))
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print("Reference:", test_dataset["sentence"])
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"""
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Example Result:
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Prediction: [
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'zieh durch bittet draußen die schuhe aus',
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'es kommt zugvorgebauten fo',
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'ihre vorterstrecken erschienen it modemagazinen wie der voge karpes basar mariclair',
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'fürliepert eine auch für manachen ungewöhnlich lange drittelliste',
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'er wurde zu ehren des reichskanzlers otto von bismarck errichtet',
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'was solls ich bin bereit',
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'das internet besteht aus vielen computern die miteinander verbunden sind',
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'der uranus ist der siebinteplanet in unserem sonnensystem s'
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]
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Reference: [
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'Zieht euch bitte draußen die Schuhe aus.',
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'Es kommt zum Showdown in Gstaad.',
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'Ihre Fotostrecken erschienen in Modemagazinen wie der Vogue, Harper’s Bazaar und Marie Claire.',
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'Felipe hat eine auch für Monarchen ungewöhnlich lange Titelliste.',
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'Er wurde zu Ehren des Reichskanzlers Otto von Bismarck errichtet.',
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'Was solls, ich bin bereit.',
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'Das Internet besteht aus vielen Computern, die miteinander verbunden sind.',
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'Der Uranus ist der siebente Planet in unserem Sonnensystem.'
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]
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"""
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```
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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 re
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import torch
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import torchaudio
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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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"""
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Evaluation on the full test set:
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- takes ~20mins (RTX 3090).
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- requires ~170GB RAM to compute the WER. Below, we use a chunked implementation of WER to avoid large RAM consumption.
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"""
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test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample
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wer = load_metric("wer")
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processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
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model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
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model.to("cuda")
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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\treturn 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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\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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\twith torch.no_grad():
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\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
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\tpred_ids = torch.argmax(logits, dim=-1)
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\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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\treturn batch
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result = test_dataset.map(evaluate, batched=True, batch_size=8) # batch_size=8 -> requires ~14.5GB GPU memory
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# non-chunked version:
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# print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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# WER: 12.900291
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# Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5:
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import jiwer
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def chunked_wer(targets, predictions, chunk_size=None):
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if chunk_size is None: return jiwer.wer(targets, predictions)
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start = 0
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end = chunk_size
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H, S, D, I = 0, 0, 0, 0
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while start < len(targets):
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chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
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H = H + chunk_metrics["hits"]
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S = S + chunk_metrics["substitutions"]
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D = D + chunk_metrics["deletions"]
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I = I + chunk_metrics["insertions"]
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start += chunk_size
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end += chunk_size
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return float(S + D + I) / float(H + S + D)
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print("Total (chunk_size=1000), WER: {:2f}".format(100 * chunked_wer(result["pred_strings"], result["sentence"], chunk_size=1000)))
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# Total (chunk=1000), WER: 12.768981
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```
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**Test Result**: WER: 12.77 %
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## Training
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The Common Voice German `train` and `validation` were used for training.
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The script used for training can be found [here](https://github.com/maxidl/wav2vec2).
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The model was trained for 50k steps, taking around 30 hours on a single A100.
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The arguments used for training this model are:
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```
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python run_finetuning.py \\
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--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
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--dataset_config_name="de" \\
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--output_dir=./wav2vec2-large-xlsr-german \\
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--preprocessing_num_workers="16" \\
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--overwrite_output_dir \\
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--num_train_epochs="20" \\
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--per_device_train_batch_size="64" \\
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--per_device_eval_batch_size="32" \\
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--learning_rate="1e-4" \\
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--warmup_steps="500" \\
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--evaluation_strategy="steps" \\
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--save_steps="5000" \\
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--eval_steps="5000" \\
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--logging_steps="1000" \\
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--save_total_limit="3" \\
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--freeze_feature_extractor \\
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--activation_dropout="0.055" \\
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--attention_dropout="0.094" \\
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--feat_proj_dropout="0.04" \\
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--layerdrop="0.04" \\
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--mask_time_prob="0.08" \\
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--gradient_checkpointing="1" \\
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--fp16 \\
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--do_train \\
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--do_eval \\
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--dataloader_num_workers="16" \\
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--group_by_length
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```
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