Fixup

app.py

@@ -4,10 +4,9 @@ import torch
 import librosa
 import json
 # Load model directly
-from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+from transformers import pipeline, AutoProcessor, AutoModelForSpeechSeq2Seq
 
-processor = AutoProcessor.from_pretrained("dmatekenya/whisper-large-v3-chichewa")
-model = AutoModelForSpeechSeq2Seq.from_pretrained("dmatekenya/whisper-large-v3-chichewa")
+pipe = pipeline("automatic-speech-recognition", model="dmatekenya/whisper-large-v3-chichewa")
 
 def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (eng)"):
     if audio_file_mic:
@@ -18,23 +17,17 @@ def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (e
         return "Please upload an audio file or record one"
 
     # Make sure audio is 16kHz
-    speech, sample_rate = librosa.load(audio_file)
-    if sample_rate != 16000:
-        speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
+    # speech, sample_rate = librosa.load(audio_file)
+    # if sample_rate != 16000:
+    #     speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
 
     # Keep the same model in memory and simply switch out the language adapters by calling load_adapter() for the model and set_target_lang() for the tokenizer
     # language_code = iso_codes[language]
     # processor.tokenizer.set_target_lang(language_code)
     # model.load_adapter(language_code)
 
-    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-
-    ids = torch.argmax(outputs, dim=-1)[0]
-    transcription = processor.decode(ids)
-    return transcription
+    result = pipe(audio_file)
+    return result["text"]
 
 
 description = ''''''