mostly adapted from cahya / wav2vec2-large-xlsr-indonesian

README.md

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+---
+language: ga
+datasets:
+- common_voice 
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+license: apache-2
+model-index:
+- name: jimregan/wav2vec2-large-xlsr-irish-basic
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice ga-IE
+      type: common_voice
+      args: ga-IE
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 49.3
+---
+# Wav2Vec2-Large-XLSR-Irish
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
+on the [Irish Common Voice dataset](https://huggingface.co/datasets/common_voice).
+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", "ga-IE", split="test[:2%]")
+processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
+model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
+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 Irish 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", "ga-IE", split="test")
+wer = load_metric("wer")
+processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
+model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic") 
+model.to("cuda")
+# So, tolower() for Irish is a bit complicated: tAthar -> t-athair
+# toupper() is non-deterministic :)
+def is_upper_vowel(letter):
+	if letter in ['A', 'E', 'I', 'O', 'U', 'Á', 'É', 'Í', 'Ó', 'Ú']:
+		return True
+	else:
+		return False
+def irish_lower(word):
+	if len(word) > 1 and word[0] in ['n', 't'] and is_upper_vowel(word[1]):
+		return word[0] + '-' + word[1:].lower()
+	else:
+		return word.lower()
+def irish_lower_sentence(sentence):
+	return " ".join([irish_lower(w) for w in sentence.split(" ")])
+chars_to_ignore_regex = '[,\?\.\!\;\:\"\“\%\‘\”\(\)\*]'
+def remove_special_characters(batch):
+	tmp = re.sub('’ ', ' ', batch["sentence"])
+	tmp = re.sub("’$", '', tmp)
+	tmp = re.sub('’', '\'', tmp)
+	tmp = re.sub(chars_to_ignore_regex, '', tmp)
+	batch["sentence"] = irish_lower_sentence(tmp) + ' '
+	return batch
+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 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"])))
+```
+**Test Result**: 49.3 %
+```