timesfm vs speech
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
speech
PyTorch toolkit for training and deploying ASR, TTS and speech LLM models
Verdict
timesfm ranks higher, 74 to 65. Of the 4 score parts we measure for both, timesfm leads on 1, speech on 1 and 2 are even.
- Adoptiontimesfm leads by 47 points, 79 to 32: a wide gap.
- FreshnessEven at 100.
- MaintenanceEven at 58.
- Agent-readyspeech leads by 55 points, 85 to 30: a wide gap.
Pick timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
Pick speech if you are
Researchers and PyTorch developers training ASR, TTS and speech LLMs
- Covers ASR, TTS and speech LLMs in one codebase with pre-trained open checkpoints
- Streaming ASR checkpoint with 80ms-1s configurable latency and 40 languages
| What we compare | timesfm | speech |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 79, popular | 32, known |
| Freshness | 100, active | 100, active |
| Maintenance | 58, fair | 58, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 34.5k | 18.6k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Sep 2026 | Oct 2026 |
| Last release | Aug 2026 | Aug 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Not stated |
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
speech
NVIDIA NeMo Speech is a Python/PyTorch toolkit for building speech models: automatic speech recognition, text-to-speech and speech LLMs. It ships training code and pre-trained checkpoints such as Parakeet, Canary, Nemotron-Speech-Streaming and MagpieTTS, published on Hugging Face. Install from source with uv, from PyPI as nemo-toolkit, or use the NGC container.
Who it is for: Researchers and PyTorch developers training ASR, TTS and speech LLMs
Strengths
- Covers ASR, TTS and speech LLMs in one codebase with pre-trained open checkpoints
- Streaming ASR checkpoint with 80ms-1s configurable latency and 40 languages
- MagpieTTS v2607 supports 12 languages; Parakeet V3 and Canary V2 cover 25 European languages
- Installs over an existing PyTorch 2.7+ build via pip without replacing it
Weaknesses
- NVIDIA GPU with CUDA required for training; only recommended for inference
- Requires Python 3.12 or newer
- Tested stack is pinned to PyTorch 2.11/2.12 with CUDA 12.9 or 13.2
- Not an inference server; no HTTP API or web UI described in the README
- GPU required
- Docker
- Needs PyTorch 2.7+, CUDA, Python 3.12+
- Models: Parakeet, Canary, Nemotron-Speech-Streaming, MagpieTTS, Canary-Qwen-2.5B
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