A 1.58-bit version of parakeet-tdt-0.6b-v3. Same architecture, same tokenizer, but every encoder weight is -1, 0 or +1. It fits in 178 MB, runs at 113× real time on eight x86 CPU cores, 2.5× the fastest other Parakeet runtime we measured, and stays within 0.3 WER of the original on English while beating it on the 25-language FLEURS set and on long-form audio. Its sibling Parakeet Ultra is the full-precision version of the same architecture, trained further, for GPUs: better than the original on every benchmark. Read the release post for the story behind both models. Run it with Photon, which reads the packed weights directly: AVX-512 VNNI on x86, NEON on ARM, Metal on Apple GPUs. Every speed number on this page is Photon.
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A post-trained version of parakeet-tdt-0.6b-v3. Same architecture, same tokenizer, same 0.6B parameters in full precision. It is better than the original on every benchmark group below, by a wide margin on the 25-language FLEURS set, in background noise and on long-form audio, and it runs faster than the original does in NeMo. Its sibling Parakeet Redux is the ternary version of the same architecture, 178 MB, built for CPUs and Apple silicon. Read the release post for the story behind both models. Run it with Photon. Every accuracy number on this page is Photon on an NVIDIA GPU.
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This is the model card for running the automatic speech recognition (ASR) models (Moonshine models) trained and released by Useful Sensors. Following Model Cards for Model Reporting (Mitchell et al.), we're providing some information about the automatic speech recognition model. More information on how these models were trained and evaluated can be found in the paper. Note, a lot of the text has been copied verbatim from the model card for the Whisper model developed by OpenAI, because both models serve identical purposes, and carry identical risks. Moonshine is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time:
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This is the model card for the Moonshine Streaming automatic speech recognition (ASR) models trained and released by Useful Sensors. Moonshine Streaming pairs a lightweight 50~Hz audio frontend with a sliding-window Transformer encoder to deliver low-latency streaming ASR on edge-class hardware. The encoder uses bounded local attention and no positional embeddings (an "ergodic" encoder), while an adapter injects positional information before a standard autoregressive decoder. This model card follows the recommendations from Model Cards for Model Reporting (Mitchell et al.). See the paper draft in this repository for full details. Moonshine Streaming is supported by the Moonshine Voice framework for edge devices and in Hugging Face Transformers. The following example matches the standard seq2seq ASR API and uses the streaming model checkpoint:
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This is the model card for the Moonshine Streaming automatic speech recognition (ASR) models trained and released by Useful Sensors. Moonshine Streaming pairs a lightweight 50~Hz audio frontend with a sliding-window Transformer encoder to deliver low-latency streaming ASR on edge-class hardware. The encoder uses bounded local attention and no positional embeddings (an "ergodic" encoder), while an adapter injects positional information before a standard autoregressive decoder. This model card follows the recommendations from Model Cards for Model Reporting (Mitchell et al.). See the paper draft in this repository for full details. Moonshine Streaming is supported in Hugging Face Transformers. The following example matches the standard seq2seq ASR API and uses the streaming model checkpoint:
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This is the model card for the Moonshine Streaming automatic speech recognition (ASR) models trained and released by Useful Sensors. Moonshine Streaming pairs a lightweight 50~Hz audio frontend with a sliding-window Transformer encoder to deliver low-latency streaming ASR on edge-class hardware. The encoder uses bounded local attention and no positional embeddings (an "ergodic" encoder), while an adapter injects positional information before a standard autoregressive decoder. This model card follows the recommendations from Model Cards for Model Reporting (Mitchell et al.). See the paper draft in this repository for full details. Moonshine Streaming is supported in Hugging Face Transformers. The following example matches the standard seq2seq ASR API and uses the streaming model checkpoint:
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This is the model card for running the automatic speech recognition (ASR) models (Moonshine models) trained and released by Useful Sensors. Following Model Cards for Model Reporting (Mitchell et al.), we're providing some information about the automatic speech recognition model. More information on how these models were trained and evaluated can be found in the paper. Note, a lot of the text has been copied verbatim from the model card for the Whisper model developed by OpenAI, because both models serve identical purposes, and carry identical risks. Moonshine is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time:
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The following datasets were used for finetuning: - Common Voice 8.0 TR All `validated` split except `test` split was used for training. To support the datasets above, custom pre-processing and loading steps was performed and wav2vec2-turkish repo was used for that purpose. The following hypermaters were used for finetuning: - learning_rate 2.5e-4 - num_train_epochs 20 - warmup_steps 500 - freeze_feature_extractor - mask_time_prob 0.1 - mask_feature_prob 0.1 - feat_proj_dropout 0.05 - attention_dropout 0.05 - final_dropout 0.1 - activation_dropout 0.05 - per_device_train_batch_size 8 - per_device_eval_batch_size 8 - gradient_accumulation_steps 8
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