Create README.md

README.md

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+---
+language: el
+datasets:
+- common_voice
+- CSS10 Greek: Single Speaker Speech Dataset
+metrics:
+- wer
+- cer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: V XLSR Wav2Vec2 Large 53 - greek
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice el
+      type: common_voice
+      args: el
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 18.996669
+       - name: Test CER
+         type: cer
+         value: 5.781874
+---
+
+# Wav2Vec2-Large-XLSR-53-greek
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice)  and [CSS10 Greek: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/greek-single-speaker-speech-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", "el", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+
+processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+
+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):
+    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"][:2], 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)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```
+
+
+## Evaluation
+
+The model can be evaluated as follows on the greek test data of Common Voice.
+
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+test_dataset = load_dataset("common_voice", "el", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data
+
+normalize_greek_letters = {"ς": "σ"}
+# normalize_greek_letters = {"ά": "α", "έ": "ε", "ί": "ι", 'ϊ': "ι", "ύ": "υ", "ς": "σ", "ΐ": "ι", 'ϋ': "υ", "ή": "η", "ώ": "ω", 'ό': "ο"}
+remove_chars_greek = {"a": "", "h": "", "n": "", "g": "", "o": "", "v": "", "e": "", "r": "", "t": "", "«": "", "»": "", "m": "", '́': '', "·": "", "’": "", '´': ""}
+replacements = {**normalize_greek_letters, **remove_chars_greek}
+
+resampler = {
+    48_000: torchaudio.transforms.Resample(48_000, 16_000),
+    44100: torchaudio.transforms.Resample(44100, 16_000),
+    32000: torchaudio.transforms.Resample(32000, 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()
+    for key, value in replacements.items():
+        batch["sentence"] = batch["sentence"].replace(key, value)
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy()
+    return batch
+
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+# Preprocessing the datasets.
+# We need to read the aduio 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)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))
+
+```
+
+**Test Result**:  18.996669 %
+
+
+## Training
+
+
+The Common Voice train dataset was used for training. Also all of `CSS10 Greek` was used using the normalized transcripts.
+During text preprocessing letter `ς` is normalized to `σ` the reason is that both letters sound the same with `ς` only used as the ending character of words. So, the change can be mapped up to proper dictation easily. I tried removing all accents from letters as well that improved `WER` significantly. The model was reaching `17%` WER easily without having converged. However, the text preprocessing needed to do after to fix transcrtiptions would be more complicated. A language model should fix things easily though. Another thing that could be tried out would be to change all of `ι`, `η` ... etc to a single character since all sound the same. similar for `o` and `ω` these should help the acoustic model part significantly since all these characters map to the same sound. But further text normlization would be needed.
+
+
+
+