whisper backend [transformers].

src/runners/audio_transcriber.py

@@ -167,7 +167,8 @@ def transcribe_audio(self, inputs: list[BatchInput]) -> list[str]:
 
 """
 
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+"""
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, WhisperProcessor, 
 import torch
 from itertools import groupby
 
@@ -230,3 +231,71 @@ def transcribe_audio(self, inputs: list[BatchInput]) -> list[str]:
             )
             for text in segments
         ]
+"""
+
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torch
+
+
+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 = WhisperProcessor.from_pretrained("openai/whisper-base.en")
+        self.model = WhisperForConditionalGeneration.from_pretrained(
+            "openai/whisper-base.en", attn_implementation="sdpa"
+        ).cuda()
+
+        self.model.generation_config.cache_implementation = "static"
+        self.model.forward = torch.compile(
+            self.model.forward, mode="reduce-overhead", fullgraph=True
+        )
+
+    def transcribe(self, audios):
+        input_features = self.processor(
+            audios, return_tensors="pt", sampling_rate=16000, padding=True
+        ).input_features.cuda()
+
+        for _ in range(2):
+            self.model.generate(input_features)
+
+        predicted_ids = self.model.generate(input_features)
+        transcriptions = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
+        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
+        ]