Create asr.py

asr.py

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+import torch
+import torchaudio
+import torchaudio.functional as AF
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+
+
+class Transcribe:
+    def __init__(self, freq: float = 16000.0) -> None:
+        self.freq = freq
+        self.model_id = "facebook/mms-1b-fl102"
+        self.processor = AutoProcessor.from_pretrained(self.model_id)
+        self.model = Wav2Vec2ForCTC.from_pretrained(self.model_id)
+
+    @torch.inference_mode()
+    def __call__(self, audio_tensor: torch.tensor, lang: str = "amh"):
+        print(lang)
+        self.processor.tokenizer.set_target_lang(lang)
+        self.model.load_adapter(lang)
+
+        outputs = self.model(audio_tensor)
+        logits = outputs.logits
+        ids = torch.argmax(logits, dim=-1)[0]
+        decoded_token = self.processor.decode(ids)
+
+        return decoded_token