update torch and meta

app.py

@@ -7,7 +7,8 @@ from datetime import datetime
 import plotly.express as px
 
 # external ASR
-from omnilingual_asr.models.inference.pipeline import ASRInferencePipeline
+import torch
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
 
 # internal modules
 from analysis import (
@@ -25,8 +26,9 @@ from region_assets import REGION_COLORS, REGION_ICONS
 # ---------------------------
 #  ASR Pipeline
 # ---------------------------
-PIPELINE = ASRInferencePipeline(model_card="omniASR_LLM_1B")
-LANG_CODE = "tur_Latn"
+MODEL_ID = "facebook/omnilingual_asr_llm_1b"
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_ID, torch_dtype=torch.float16).to("cuda")
 
 
 # ---------------------------
@@ -166,16 +168,28 @@ def full_process(audio, region):
 
     ref = TEST_SENTENCES[region]
 
-    # ASR
+    # ASR via transformers pipeline
     try:
-        hyp_list = PIPELINE.transcribe(
-            [{"waveform": data, "sample_rate": sr}],
-            lang=[LANG_CODE],
-            batch_size=1
+    # 1. Input features hazırlama
+        inputs = processor(
+            data,
+            sampling_rate=sr,
+            return_tensors="pt"
+        ).to("cuda")
+
+    # 2. Model ile inference
+    with torch.no_grad():
+        generated_ids = model.generate(
+            inputs.input_features,
+            max_length=400
         )
-        hyp = hyp_list[0] if hyp_list else ""
+
+    # 3. Decode
+    hyp = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
     except Exception as e:
-        return (f"ASR hata: {e}", None, None, None, None, None, None, None, None, None)
+        return (f"ASR Hatası: {e}", None, None, None, None, None, None, None, None, None)
+
 
     # basic metrics
     _cer = cer(ref, hyp)