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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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- 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 |
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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: 15.80 |
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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[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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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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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"][:2], 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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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the {language} test data of Common Voice. |
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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, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "de", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]' |
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substitutions = { |
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'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]', |
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'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]', |
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'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]', |
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'c' : '[\č\ć\ç\с]', |
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'l' : '[\ł]', |
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'u' : '[\ú\ū\ứ\ů]', |
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'und' : '[\&]', |
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'r' : '[\ř]', |
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'y' : '[\ý]', |
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's' : '[\ś\š\ș\ş]', |
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'i' : '[\ī\ǐ\í\ï\î\ï]', |
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'z' : '[\ź\ž\ź\ż]', |
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'n' : '[\ñ\ń\ņ]', |
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'g' : '[\ğ]', |
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'ss' : '[\ß]', |
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't' : '[\ț\ť]', |
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'd' : '[\ď\đ]', |
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"'": '[\ʿ\་\’\`\´\ʻ\`\‘]', |
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'p': '\р' |
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} |
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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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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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for x in substitutions: |
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batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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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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# Preprocessing the datasets. |
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# We need to read the aduio 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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The model can also be evaluated with in 10% chunks which needs less ressources (to be tested). |
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``` |
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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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import re |
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import jiwer |
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lang_id = "de" |
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processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-53-german") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]' |
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substitutions = { |
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'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]', |
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'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]', |
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'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]', |
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'c' : '[\č\ć\ç\с]', |
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'l' : '[\ł]', |
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'u' : '[\ú\ū\ứ\ů]', |
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'und' : '[\&]', |
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'r' : '[\ř]', |
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'y' : '[\ý]', |
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's' : '[\ś\š\ș\ş]', |
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'i' : '[\ī\ǐ\í\ï\î\ï]', |
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'z' : '[\ź\ž\ź\ż]', |
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'n' : '[\ñ\ń\ņ]', |
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'g' : '[\ğ]', |
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'ss' : '[\ß]', |
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't' : '[\ț\ť]', |
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'd' : '[\ď\đ]', |
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"'": '[\ʿ\་\’\`\´\ʻ\`\‘]', |
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'p': '\р' |
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} |
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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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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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for x in substitutions: |
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batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"]) |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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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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# Preprocessing the datasets. |
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# We need to read the aduio 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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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H, S, D, I = 0, 0, 0, 0 |
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for i in range(10): |
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print("test["+str(10*i)+"%:"+str(10*(i+1))+"%]") |
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test_dataset = load_dataset("common_voice", "de", split="test["+str(10*i)+"%:"+str(10*(i+1))+"%]") |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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predictions = result["pred_strings"] |
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targets = result["sentence"] |
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chunk_metrics = jiwer.compute_measures(targets, predictions) |
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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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WER = float(S + D + I) / float(H + S + D) |
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print("WER: {:2f}".format(WER*100)) |
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``` |
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**Test Result**: 15.80 % |
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## Training |
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The first 50% of the Common Voice `train`, and 12% of the `validation` datasets were used for training (30 epochs on first 12% and 3 epochs on the remainder). |
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