Update app.py

app.py

@@ -18,36 +18,31 @@ from datasets import set_caching_enabled
 set_caching_enabled(False)
 disable_caching()
 
-p = pipeline("automatic-speech-recognition")
-
-#config = AutoConfig.from_pretrained('whisper-small')
+from transformers import pipeline, WhisperModel, WhisperTokenizer, AutoConfig
+from datasets import load_dataset
+from transformers import WERMetric
 
+# Load the Whisper model and tokenizer
 huggingface_token = os.environ["huggingface_token"]
-
-whisper_miso=WhisperModel.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
+whisper_miso = WhisperModel.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
 miso_tokenizer = WhisperTokenizer.from_pretrained("mskov/whisper_miso", use_auth_token=huggingface_token)
-#miso_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", use_auth_token=huggingface_token)
 
-task_evaluator = p.task_evaluator
-task_evaluator.feature_extractor = whisper_miso.get_feature_extractor()
+# Initialize the automatic-speech-recognition pipeline with the Whisper model and tokenizer
+asr_pipeline = pipeline(
+    "automatic-speech-recognition",
+    model=whisper_miso,
+    tokenizer=miso_tokenizer
+)
 
-task_evaluator = evaluator("automatic-speech-recognition")
-#url = {"test" : "https://huggingface.co/datasets/mskov/miso_test/blob/main/test_set.parquet"}
-#data = load_dataset("audiofolder", data_dir="mskov/miso_test")
-# data = load_dataset("audiofolder", data_files=["datasets/mskov/miso_test/test_set/and.wav","mskov/miso_test/test_set/chew1.wav","mskov/miso_test/test_set/chew3.wav", "mskov/miso_test/test_set/chew3.wav","mskov/miso_test/test_set/chew4.wav","mskov/miso_test/test_set/cough1.wav","mskov/miso_test/test_set/cough2.wav","mskov/miso_test/test_set/cough3.wav","mskov/miso_test/test_set/hi.wav","mskov/miso_test/test_set/knock_knock.wav","mskov/miso_test/test_set/mouth_sounds1.wav","mskov/miso_test/test_set/mouth_sounds2.wav","mskov/miso_test/test_set/no.wav","mskov/miso_test/test_set/not_bad.wav","mskov/miso_test/test_set/oh_i_wish.wav","mskov/miso_test/test_set/pop1.wav","mskov/miso_test/test_set/really.wav","mskov/miso_test/test_set/sigh1.wav","mskov/miso_test/test_set/sigh2.wav","mskov/miso_test/test_set/slurp1.wav","mskov/miso_test/test_set/slurp2.wav","mskov/miso_test/test_set/sneeze1.wav","mskov/miso_test/test_set/sneeze2.wav","mskov/miso_test/test_set/so_i_did_it_again.wav"])
+# Load the dataset
 dataset = load_dataset("mskov/miso_test", split="test").cast_column("audio", Audio())
-results = task_evaluator.compute(
-    model_or_pipeline=whisper_miso,
-    #model_or_pipeline="mskov/whisper-small.en",
-    data=dataset,
-    tokenizer=miso_tokenizer,
-    input_column="audio",
-    label_column="audio",
-    # device=None,
-    strategy="simple",
-    metric="wer",
-)
-print(results)
+
+# Compute the evaluation results
+results = asr_pipeline(dataset)
+metric = WERMetric()
+wer = metric.compute(predictions=results, references=dataset["audio"])
+print(wer)
+
 
 
 def transcribe(audio, state=""):