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README.md

@@ -1 +1,135 @@
-TODO
+---
+language: lv
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: Latvian XLSR Wav2Vec2 Large 53 by Anton Lozhkov
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice lv
+      type: common_voice
+      args: lv
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 26.89
+---
+
+# Wav2Vec2-Large-XLSR-53-Latvian
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Latvian 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", "lv", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
+model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the audio 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 Latvian test data of Common Voice.
+
+```python
+import torch
+import torchaudio
+import urllib.request
+import tarfile
+import pandas as pd
+from tqdm.auto import tqdm
+from datasets import load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# Download the raw data instead of using HF datasets to save disk space 
+data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/lv.tar.gz"
+filestream = urllib.request.urlopen(data_url)
+data_file = tarfile.open(fileobj=filestream, mode="r|gz")
+data_file.extractall()
+
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
+model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
+model.to("cuda")
+
+cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/lv/test.tsv", sep='\t')
+clips_path = "cv-corpus-6.1-2020-12-11/lv/clips/"
+
+def clean_sentence(sent):
+    sent = sent.lower()
+    # replace non-alpha characters with space
+    sent = "".join(ch if ch.isalpha() else " " for ch in sent)
+    # remove repeated spaces
+    sent = " ".join(sent.split())
+    return sent
+
+targets = []
+preds = []
+
+for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
+    row["sentence"] = clean_sentence(row["sentence"])
+    speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
+    row["speech"] = resampler(speech_array).squeeze().numpy()
+
+    inputs = processor(row["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)
+
+    targets.append(row["sentence"])
+    preds.append(processor.batch_decode(pred_ids)[0])
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
+```
+
+**Test Result**: 26.89 %  
+
+
+## Training
+
+The Common Voice `train` and `validation` datasets were used for training.