Update app.py

app.py

@@ -4,6 +4,7 @@ os.system("pip install transformers==4.27.0")
 os.system("pip install numpy==1.23")
 from transformers import pipeline, WhisperModel, WhisperTokenizer, WhisperFeatureExtractor, AutoFeatureExtractor, AutoProcessor, WhisperConfig
 os.system("pip install jiwer")
+from jiwer import wer
 os.system("pip install datasets[audio]")
 from evaluate import evaluator
 from datasets import load_dataset, Audio, disable_caching, set_caching_enabled
@@ -15,13 +16,36 @@ huggingface_token = os.environ["huggingface_token"]
 
 model = WhisperModel.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
 feature_extractor = AutoFeatureExtractor.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
+miso_tokenizer = WhisperTokenizer.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
 
+dataset = load_dataset("mskov/miso_test", split="test").cast_column("audio", Audio(sampling_rate=16000))
 
-ds = load_dataset("mskov/miso_test", split="test").cast_column("audio", Audio(sampling_rate=16000))
+print(dataset, "and at 0 ", dataset[0])
 
-print(ds, "and at 0 ", ds[0])
+inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")
+tokenized_dataset = miso_tokenizer(dataset)  # Tokenize the dataset
 
-inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+input_ids = features.input_ids
+attention_mask = features.attention_mask
+
+# Evaluate the model
+model.eval()
+with torch.no_grad():
+    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+
+# Convert predicted token IDs back to text
+predicted_text = tokenizer.batch_decode(outputs.logits.argmax(dim=-1), skip_special_tokens=True)
+
+# Get ground truth labels from the dataset
+labels = dataset["audio"]  # Replace "labels" with the appropriate key in your dataset
+
+# Compute WER
+wer_score = wer(labels, predicted_text)
+
+# Print or return WER score
+print(f"Word Error Rate (WER): {wer_score}")
+
+'''
 print("check check")
 print(inputs)
 input_features = inputs.input_features
@@ -29,3 +53,4 @@ decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
 last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
 list(last_hidden_state.shape)
 print(list(last_hidden_state.shape))
+'''