add eval

eval.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+from datasets import load_dataset, load_metric, Audio
+from transformers import AutoModelForCTC, AutoProcessor, Wav2Vec2Processor
+import torch
+
+lang = "sv-SE"
+model_id = "./xls-r-300m-sv"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+dataset = load_dataset("mozilla-foundation/common_voice_7_0", lang, split="test", use_auth_token=True)
+wer = load_metric("wer")
+
+dataset = dataset.select(range(100))
+
+dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
+
+model = AutoModelForCTC.from_pretrained(model_id).to(device)
+processor = Wav2Vec2Processor.from_pretrained(model_id)
+
+
+def map_to_pred(batch):
+    input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest", sampling_rate=16_000).input_values
+
+    with torch.no_grad():
+        logits = model(input_values.to(device)).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0]
+    batch["transcription"] = transcription
+    return batch
+
+
+result = dataset.map(map_to_pred, remove_columns=["audio"])
+
+import ipdb; ipdb.set_trace()
+wer_result = wer.compute(references=result["sentence"], predictions=result["transcription"])
+
+print("WER", wer_result)