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Deploy fully self-trained Vanta (separator + speaker encoder)
Komalpreet KaurUnverified signatureReplaces the pretrained ECAPA with our own trained speaker encoder, so no pretrained weights remain in the model. On 500 held-out mixtures the pair beats the previous deployment: SI-SDR +8.45 vs +7.93, PESQ 1.247 vs 1.182, STOI 0.751 vs 0.739. CPU inference is also ~6x faster without speechbrain's ECAPA. The separator was trained against this exact encoder, so both checkpoints ship together and must stay paired. Includes a decode fix for MP4/MOV whose moov atom follows the media data: those were piped to ffmpeg, which cannot rewind a pipe to find the index, and decoded to zero samples while exiting 0. Phone recordings often lack faststart, so this affected real uploads.1bd371e
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Deploy from-scratch trained separator (val SI-SDR +7.93 dB)
Komalpreet KaurUnverified signatureSwitches the Space default from the pretrained SepFormer backbone to the model trained from scratch: 952 speakers (LibriSpeech + AMI conversational), WHAM real noise, real and simulated RIRs, turn-taking, recording-chain degradation. SepFormer still ships in the image and is available via VANTA_BACKEND=sepformer without a rebuild. Also fixes a device desync where speechbrain's ECAPA wrapper kept its own device attribute, which crashed CPU-only inference (as the Space runs).e1de6b8
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Initial deploy: Vanta TSE backend (FastAPI + trained checkpoint)
Komalpreet KaurUnverified signature32de4f6