That model collapse argument assumes pre-training teams are just scraping raw web garbage without curation.
Are you running the Moonshine model via ONNX/CoreML or native ggml/mlx bindings? how is the first token latency and memory footprint compared against Whisper small.en on Apple Silicon
Are you processing the transcription and synthesis through local models for HIPAA/BAA compliance, or running PII redaction before hitting cloud APIs?
That model collapse argument assumes pre-training teams are just scraping raw web garbage without curation.
Are you running the Moonshine model via ONNX/CoreML or native ggml/mlx bindings? how is the first token latency and memory footprint compared against Whisper small.en on Apple Silicon
Are you processing the transcription and synthesis through local models for HIPAA/BAA compliance, or running PII redaction before hitting cloud APIs?