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Check out the documentation for more information.
Parakeet TDT v2 OpenVINO Models
Downloaded from: https://huggingface.co/FluidInference/parakeet-tdt-0.6b-v2-ov
Model Files
Preprocessor (Mel Spectrogram)
parakeet_melspectogram.xml- Model architectureparakeet_melspectogram.bin- Weights (66 KB)
Encoder (Acoustic Model)
parakeet_encoder.xml- Model architectureparakeet_encoder.bin- Weights (1.1 GB)
Decoder (LSTM Language Model)
parakeet_decoder.xml- Model architectureparakeet_decoder.bin- Weights (14 MB)
Joint Network (Token + Duration Prediction)
parakeet_joint.xml- Model architectureparakeet_joint.bin- Weights (3.3 MB)
Vocabulary
parakeet_vocab.json- SentencePiece vocabulary (1024 tokens + blank)
Model Configuration
- Model Version: Parakeet TDT 0.6b v2
- Blank Token ID: 1024
- Vocabulary Size: 1024 regular tokens + 1 blank token
- Duration Bins: [0, 1, 2, 3, 4] frames
- Sample Rate: 16000 Hz
- Frame Duration: 80ms per encoder frame
Usage Example
See docs/parakeet_openvino.md for usage examples.
Basic usage:
#include "eddy/models/parakeet/parakeet_openvino.hpp"
auto backend = std::make_shared<eddy::OpenVINOBackend>(
eddy::OpenVINOOptions{.device = "CPU"}
);
eddy::parakeet::ModelPaths paths{
.preprocessor = {.path = "models/parakeet/parakeet_melspectogram.xml"},
.encoder = {.path = "models/parakeet/parakeet_encoder.xml"},
.decoder = {.path = "models/parakeet/parakeet_decoder.xml"},
.joint = {.path = "models/parakeet/parakeet_joint.xml"},
.tokenizer_json = "models/parakeet/parakeet_vocab.json"
};
eddy::parakeet::RuntimeConfig cfg{
.device = "CPU",
.blank_token_id = 1024,
.duration_bins = {0, 1, 2, 3, 4}
};
auto model = eddy::parakeet::make_openvino_parakeet(backend, paths, cfg);
model->warmup();
// Transcribe audio
eddy::parakeet::AudioSegment segment;
segment.sample_rate = 16000;
segment.pcm = /* your 16kHz mono float32 audio */;
auto result = model->infer(segment, {});
std::cout << "Transcription: " << result.text << "\n";
Next Steps
See docs/GETTING_STARTED_PARAKEET_CPP.md for a complete implementation guide.
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