Create asr.py

asr.py

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+import librosa
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+
+ASR_SAMPLING_RATE = 16_000
+MODEL_ID = "facebook/mms-1b-all"
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+
+def transcribe(audio_source=None, microphone=None, file_upload=None):
+    audio_fp = file_upload if "upload" in str(audio_source or "").lower() 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_fp, sr=ASR_SAMPLING_RATE, mono=True)[0]
+    processor.tokenizer.set_target_lang("fao")  # Set Faroese language
+    model.load_adapter("fao")
+
+    inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
+
+    # Set device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    inputs = inputs.to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+
+    ids = torch.argmax(outputs, dim=-1)[0]
+    transcription = processor.decode(ids)
+
+    return transcription