diff --git a/env/python/requirements.txt b/env/python/requirements.txt
index 5dab702..4eea27a 100644
--- a/env/python/requirements.txt
+++ b/env/python/requirements.txt
@@ -7,3 +7,5 @@ pydantic
 pydantic-settings
 six
 msgspec
+transformers
+torch
\ No newline at end of file
diff --git a/src/runners/audio_transcriber.py b/src/runners/audio_transcriber.py
index 52cbc06..fa6d7b5 100644
--- a/src/runners/audio_transcriber.py
+++ b/src/runners/audio_transcriber.py
@@ -75,6 +75,7 @@ def add(self, word):
             self.transcription += word.word
 
 
+"""
 class BatchableAudioTranscriber(bentoml.Runnable):
     SUPPORTED_RESOURCES = ("nvidia.com/gpu", "cpu")
     SUPPORTS_CPU_MULTI_THREADING = True
@@ -163,3 +164,69 @@ def transcribe_audio(self, inputs: list[BatchInput]) -> list[str]:
             )
 
         return result
+
+"""
+
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import torch
+from itertools import groupby
+
+
+class BatchableAudioTranscriber(bentoml.Runnable):
+    SUPPORTED_RESOURCES = ("nvidia.com/gpu", "cpu")
+    SUPPORTS_CPU_MULTI_THREADING = True
+
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.processor = Wav2Vec2Processor.from_pretrained(
+            "facebook/wav2vec2-base-960h"
+        )
+        self.model = Wav2Vec2ForCTC.from_pretrained(
+            "facebook/wav2vec2-base-960h"
+        ).cuda()
+
+    def transcribe(self, audios):
+        input_values = self.processor(
+            audios, return_tensors="pt", sampling_rate=16000
+        ).input_values.cuda()
+
+        with torch.no_grad():
+            logits = self.model(input_values).logits
+        predicted_ids = torch.argmax(logits, dim=-1)
+        transcriptions = self.processor.batch_decode(predicted_ids)
+        print(transcriptions)
+
+        return transcriptions
+
+    @bentoml.Runnable.method(batchable=True)
+    def transcribe_audio(self, inputs: list[BatchInput]) -> list[str]:
+        result = []
+
+        # merging audio
+        ts = datetime.now()
+        audio_batch = []
+        for input in inputs:
+            wav = decode_audio(io.BytesIO(input.audio))
+            chunks = get_speech_timestamps(wav)
+            if len(chunks) == 0:
+                audio_batch.append(np.zeros(16000, dtype=np.float32))
+            else:
+                wav = collect_chunks(wav, chunks=chunks)
+                audio_batch.append(wav)
+
+        merge_time = (datetime.now() - ts).total_seconds()
+
+        ts = datetime.now()
+        segments = self.transcribe(audio_batch)
+        transcribe_time = (datetime.now() - ts).total_seconds()
+
+        return [
+            BatchOutput(
+                text=text,
+                batched_count=len(inputs),
+                merge_audio_time=merge_time,
+                transcribe_time=transcribe_time,
+                restore_time=0,
+            )
+            for text in segments
+        ]