Emerald7664/ldtm
Updated
Rinat D
Emerald7664
AI & ML interests
None yet
Recent Activity
updated a model about 6 hours ago
Emerald7664/ldtm published a model about 6 hours ago
Emerald7664/ldtm liked a model about 10 hours ago
Vortex5/Fenrir-X-26B-A4BOrganizations
updated a
model about 6 hours ago
published a
model about 6 hours ago
liked 2
models about 10 hours ago
liked a
model 1 day ago
liked 2
models 11 days ago
liked a
model 12 days ago
updated a
model 13 days ago
reacted to owensong's post with 🔥 14 days ago
Post
7056
I just released Inflect v2, pushing complete TTS below 4M parameters.
Inflect-Nano-v2 fits every learned inference component, including waveform generation, into 3.96M parameters (15.97 MB FP32), making it one of the smallest publicly available complete TTS models. Inflect-Micro-v2 stays under 10M at 9.36M parameters (37.53 MB), using the extra capacity for clearer, more natural speech.
This was not about building a toy that technically produces audio. I wanted to see how much useful TTS could actually fit at this scale.
Nano stayed surprisingly close to Micro across the reported release metrics, but if you want to hear Inflect at its best, start with Micro:
owensong/Inflect-Micro-v2
Text goes in and 24 kHz speech comes out without a second learned model. Both models have PyTorch and ONNX releases: PyTorch runs on CPU or CUDA, while the browser demo runs ONNX locally through WebGPU with a WASM fallback. The Inflect code and weights are released under Apache 2.0.
Try Nano and Micro side by side:
owensong/Inflect-v2
Some people may remember the much rougher Inflect-Nano-v1 experiment. V2 was rebuilt and retrained to address its biggest weaknesses: metallic audio, unstable timing, weak prosody, and difficulty with unseen text. Nano-v2 is smaller while sounding substantially cleaner and more stable.
In an anonymous community blind test across nine voices, Micro and Nano placed second and third by normalized preference rate. Full results, samples, limitations, and inference instructions are on the model pages.
Inflect-Nano-v2:
owensong/Inflect-Nano-v2
I built Inflect independently with a limited training budget. If you test it, give it something difficult: unusual names, numbers, abbreviations, symbols, or awkward punctuation. Send me the exact text when something breaks. If you build something with it, I would genuinely love to see it.
Inflect-Nano-v2 fits every learned inference component, including waveform generation, into 3.96M parameters (15.97 MB FP32), making it one of the smallest publicly available complete TTS models. Inflect-Micro-v2 stays under 10M at 9.36M parameters (37.53 MB), using the extra capacity for clearer, more natural speech.
This was not about building a toy that technically produces audio. I wanted to see how much useful TTS could actually fit at this scale.
Nano stayed surprisingly close to Micro across the reported release metrics, but if you want to hear Inflect at its best, start with Micro:
owensong/Inflect-Micro-v2
Text goes in and 24 kHz speech comes out without a second learned model. Both models have PyTorch and ONNX releases: PyTorch runs on CPU or CUDA, while the browser demo runs ONNX locally through WebGPU with a WASM fallback. The Inflect code and weights are released under Apache 2.0.
Try Nano and Micro side by side:
owensong/Inflect-v2
Some people may remember the much rougher Inflect-Nano-v1 experiment. V2 was rebuilt and retrained to address its biggest weaknesses: metallic audio, unstable timing, weak prosody, and difficulty with unseen text. Nano-v2 is smaller while sounding substantially cleaner and more stable.
In an anonymous community blind test across nine voices, Micro and Nano placed second and third by normalized preference rate. Full results, samples, limitations, and inference instructions are on the model pages.
Inflect-Nano-v2:
owensong/Inflect-Nano-v2
I built Inflect independently with a limited training budget. If you test it, give it something difficult: unusual names, numbers, abbreviations, symbols, or awkward punctuation. Send me the exact text when something breaks. If you build something with it, I would genuinely love to see it.
- 1 reply
liked a
model 14 days ago
updated a
model 16 days ago
liked 2
models 17 days ago
liked 2
models 19 days ago
published a
model 24 days ago
reacted to Hoglet-33's post with 🔥 26 days ago
Post
3318
Pebble 10M and Pebble 10M Chat are now released!
Both models use our Mamba/Transformer 3:1 hybrid architecture and were pretrained on 25 billion tokens.
Pebble 10M Chat was additionally fine-tuned on 250 million tokens of Smol-SmolTalk to improve its conversational capabilities.
You can find them here:
- basically-ai/Pebble-10M
- basically-ai/Pebble-10M-Chat
We hope you enjoy using them. The rest of the Pebble family will be released soon.
Follow for more:
@Hoglet-33
basically-ai
Both models use our Mamba/Transformer 3:1 hybrid architecture and were pretrained on 25 billion tokens.
Pebble 10M Chat was additionally fine-tuned on 250 million tokens of Smol-SmolTalk to improve its conversational capabilities.
You can find them here:
- basically-ai/Pebble-10M
- basically-ai/Pebble-10M-Chat
We hope you enjoy using them. The rest of the Pebble family will be released soon.
Follow for more:
@Hoglet-33
- 32 replies
updated a
model about 1 month ago
liked a
model about 1 month ago
