mirror of
https://github.com/blakeblackshear/frigate.git
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c371fc0c87
* Don't warn when event ids have expired for trigger sync * Import faster_whisper conditinally to avoid illegal instruction * Catch OpenVINO runtime error * fix race condition in detail stream context navigating between tracked objects in Explore would sometimes prevent the object track from appearing * Handle case where classification images are deleted * Adjust default rounded corners on larger screens * Improve flow handling for classification state * Remove images when wizard is cancelled * Improve deletion handling for classes * Set constraints on review buffers * Update to support correct data format * Set minimum duration for recording based review items * Use friendly name in review genai prompt --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
84 lines
3.6 KiB
Python
84 lines
3.6 KiB
Python
"""Set up audio transcription models based on model size."""
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import logging
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import os
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import sherpa_onnx
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.const import MODEL_CACHE_DIR
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from frigate.data_processing.types import AudioTranscriptionModel
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from frigate.util.downloader import ModelDownloader
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logger = logging.getLogger(__name__)
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class AudioTranscriptionModelRunner:
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def __init__(
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self,
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device: str = "CPU",
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model_size: str = "small",
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):
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self.model: AudioTranscriptionModel = None
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self.requestor = InterProcessRequestor()
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if model_size == "large":
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# use the Whisper download function instead of our own
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# Import dynamically to avoid crashes on systems without AVX support
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from faster_whisper.utils import download_model
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logger.debug("Downloading Whisper audio transcription model")
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download_model(
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size_or_id="small" if device == "cuda" else "tiny",
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local_files_only=False,
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cache_dir=os.path.join(MODEL_CACHE_DIR, "whisper"),
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)
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logger.debug("Whisper audio transcription model downloaded")
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else:
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# small model as default
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download_path = os.path.join(MODEL_CACHE_DIR, "sherpa-onnx")
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HF_ENDPOINT = os.environ.get("HF_ENDPOINT", "https://huggingface.co")
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self.model_files = {
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"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",
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"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",
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"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",
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"tokens.txt": f"{HF_ENDPOINT}/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/tokens.txt",
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}
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if not all(
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os.path.exists(os.path.join(download_path, n))
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for n in self.model_files.keys()
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):
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self.downloader = ModelDownloader(
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model_name="sherpa-onnx",
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download_path=download_path,
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file_names=self.model_files.keys(),
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download_func=self.__download_models,
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)
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self.downloader.ensure_model_files()
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self.downloader.wait_for_download()
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self.model = sherpa_onnx.OnlineRecognizer.from_transducer(
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tokens=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/tokens.txt"),
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encoder=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/encoder.onnx"),
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decoder=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/decoder.onnx"),
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joiner=os.path.join(MODEL_CACHE_DIR, "sherpa-onnx/joiner.onnx"),
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num_threads=2,
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sample_rate=16000,
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feature_dim=80,
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enable_endpoint_detection=True,
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rule1_min_trailing_silence=2.4,
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rule2_min_trailing_silence=1.2,
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rule3_min_utterance_length=300,
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decoding_method="greedy_search",
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provider="cpu",
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)
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def __download_models(self, path: str) -> None:
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try:
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file_name = os.path.basename(path)
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ModelDownloader.download_from_url(self.model_files[file_name], path)
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except Exception as e:
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logger.error(f"Failed to download {path}: {e}")
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