Create README.md

README.md

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+---
+language: eo
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: Wav2Vec2 Large 53 Esperanto by Gunjan Chhablani
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice eo
+      type: common_voice
+      args: eo
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 9.92
+---
+
+# Wav2Vec2-Large-XLSR-53-Esperanto
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Esperanto 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", "eo", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo')
+model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo')
+
+
+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 Portuguese test data of Common Voice.
+
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+import jiwer
+
+def chunked_wer(targets, predictions, chunk_size=None):
+    if chunk_size is None: return jiwer.wer(targets, predictions)
+    start = 0
+    end = chunk_size
+    H, S, D, I = 0, 0, 0, 0
+    while start < len(targets):
+        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
+        H = H + chunk_metrics["hits"]
+        S = S + chunk_metrics["substitutions"]
+        D = D + chunk_metrics["deletions"]
+        I = I + chunk_metrics["insertions"]
+        start += chunk_size
+        end += chunk_size
+    return float(S + D + I) / float(H + S + D)
+    
+test_dataset = load_dataset("common_voice", "eo", 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('gchhablani/wav2vec2-large-xlsr-eo')
+model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo')
+
+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().replace('—',' ').replace('–',' ')
+    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 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000)))
+```
+
+**Test Result**: 9.92 % 
+
+## Training
+
+The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-esperanto-asr-with-transformers-final.ipynb).