Update asr.py

asr.py

@@ -3,16 +3,22 @@ from transformers import Wav2Vec2ForCTC, AutoProcessor
 import torch
 
 ASR_SAMPLING_RATE = 16_000
-MODEL_ID = "facebook/mms-1b-all"
 
+MODEL_ID = "facebook/mms-1b-all"
 processor = AutoProcessor.from_pretrained(MODEL_ID)
 model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
 
-def transcribe(audio):
-    if audio is None:
+def transcribe(audio_source=None, microphone=None, file_upload=None):
+    audio_fp = file_upload if file_upload else microphone
+    if audio_fp is None:
         return "ERROR: You have to either use the microphone or upload an audio file"
     
-    audio_samples = librosa.load(audio, sr=ASR_SAMPLING_RATE, mono=True)[0]
+    audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0]
+
+    # Set Faroese language
+    processor.tokenizer.set_target_lang("fao")
+    model.load_adapter("fao")
+
     inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -21,12 +27,7 @@ def transcribe(audio):
 
     with torch.no_grad():
         outputs = model(**inputs).logits
+        ids = torch.argmax(outputs, dim=-1)[0]
+        transcription = processor.decode(ids)
 
-    # Setting the target language to Faroese (ISO 639-3: fao)
-    processor.tokenizer.set_target_lang("fao")
-    model.load_adapter("fao")
-    
-    ids = torch.argmax(outputs, dim=-1)[0]
-    transcription = processor.decode(ids)
-    
     return transcription