Audio transcription tweaks (#18540)

* use model runner

* unload whisper model when live transcription is complete

frigate/data_processing/common/audio_transcription/model.py

@@ -0,0 +1,81 @@
+"""Set up audio transcription models based on model size."""
+
+import logging
+import os
+
+import sherpa_onnx
+from faster_whisper.utils import download_model
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.const import MODEL_CACHE_DIR
+from frigate.data_processing.types import AudioTranscriptionModel
+from frigate.util.downloader import ModelDownloader
+
+logger = logging.getLogger(__name__)
+
+
+class AudioTranscriptionModelRunner:
+    def __init__(
+        self,
+        device: str = "CPU",
+        model_size: str = "small",
+    ):
+        self.model: AudioTranscriptionModel = None
+        self.requestor = InterProcessRequestor()
+
+        if model_size == "large":
+            # use the Whisper download function instead of our own
+            logger.debug("Downloading Whisper audio transcription model")
+            download_model(
+                size_or_id="small" if device == "cuda" else "tiny",
+                local_files_only=False,
+                cache_dir=os.path.join(MODEL_CACHE_DIR, "whisper"),
+            )
+            logger.debug("Whisper audio transcription model downloaded")
+
+        else:
+            # small model as default
+            download_path = os.path.join(MODEL_CACHE_DIR, "sherpa-onnx")
+            HF_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
+            self.model_files = {
+                "encoder.onnx": f"{HF_ENDPOINT}/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/encoder-epoch-99-avg-1-chunk-16-left-128.onnx",
+                "decoder.onnx": f"{HF_ENDPOINT}/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/decoder-epoch-99-avg-1-chunk-16-left-128.onnx",
+                "joiner.onnx": f"{HF_ENDPOINT}/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/joiner-epoch-99-avg-1-chunk-16-left-128.onnx",
+                "tokens.txt": f"{HF_ENDPOINT}/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/tokens.txt",
+            }
+
+            if not all(
+                os.path.exists(os.path.join(download_path, n))
+                for n in self.model_files.keys()
+            ):
+                self.downloader = ModelDownloader(
+                    model_name="sherpa-onnx",
+                    download_path=download_path,
+                    file_names=self.model_files.keys(),
+                    download_func=self.__download_models,
+                )
+                self.downloader.ensure_model_files()
+                self.downloader.wait_for_download()
+
+            self.model = sherpa_onnx.OnlineRecognizer.from_transducer(
+                tokens=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/tokens.txt"),
+                encoder=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/encoder.onnx"),
+                decoder=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/decoder.onnx"),
+                joiner=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/joiner.onnx"),
+                num_threads=2,
+                sample_rate=16000,
+                feature_dim=80,
+                enable_endpoint_detection=True,
+                rule1_min_trailing_silence=2.4,
+                rule2_min_trailing_silence=1.2,
+                rule3_min_utterance_length=300,
+                decoding_method="greedy_search",
+                provider="cpu",
+            )
+
+    def __download_models(self, path: str) -> None:
+        try:
+            file_name = os.path.basename(path)
+            ModelDownloader.download_from_url(self.model_files[file_name], path)
+        except Exception as e:
+            logger.error(f"Failed to download {path}: {e}")