--- title: "LLMs-from-scratch vs speech_recognition" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rasbt-llms-from-scratch-vs-uberi-speech-recognition" tools: ["rasbt-llms-from-scratch", "uberi-speech-recognition"] --- # LLMs-from-scratch vs speech_recognition *GraphCanon updated Jul 11, 2026* ## Verdict Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; speech_recognition is Python; pick speech_recognition when speech_recognition is primarily Python; LLMs-from-scratch is Jupyter Notebook. [LLMs-from-scratch](https://amzn.to/4fqvn0D) reports 99k GitHub stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. [speech_recognition](https://pypi.python.org/pypi/SpeechRecognition/) has 9.0k stars, 2.4k forks, and 317 open issues, last pushed Jun 16, 2026. Figures are from public GitHub metadata via [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch) and [speech_recognition's repository](https://github.com/Uberi/speech_recognition). | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [speech_recognition](/tools/uberi-speech-recognition.md) | | --- | --- | --- | | Tagline | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | Speech recognition module for Python, supporting several engines and APIs, online and offline. | | Stars | 98,899 | 8,971 | | Forks | 15,183 | 2,417 | | Open issues | 4 | 317 | | Language | Jupyter Notebook | Python | | Adopt for | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | - | | Persona | - | - | | Runtime | - | - | | License | Other | BSD-3-Clause | | Categories | LLM Frameworks, Model Training | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [speech_recognition](/tools/uberi-speech-recognition.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 38d | 24d | | Open issues (now) | 4 | 317 | | Full report | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | [trust report](/tools/uberi-speech-recognition/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; speech_recognition is Python. - License: LLMs-from-scratch is Other, speech_recognition is BSD-3-Clause. - Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### Choose speech_recognition if… - speech_recognition is primarily Python; LLMs-from-scratch is Jupyter Notebook. - License: speech_recognition is BSD-3-Clause, LLMs-from-scratch is Other. - Tags unique to speech_recognition: speech-to-text, python, audio, speech-recognition. - Also covers AI Agents. ## When NOT to use LLMs-from-scratch - - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 - a deeper learning experience. ## When NOT to use speech_recognition - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between LLMs-from-scratch and speech_recognition? LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. speech_recognition: Speech recognition module for Python, supporting several engines and APIs, online and offline.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMs-from-scratch over speech_recognition? Choose LLMs-from-scratch over speech_recognition when LLMs-from-scratch is primarily Jupyter Notebook; speech_recognition is Python; License: LLMs-from-scratch is Other, speech_recognition is BSD-3-Clause; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I choose speech_recognition over LLMs-from-scratch? Choose speech_recognition over LLMs-from-scratch when speech_recognition is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: speech_recognition is BSD-3-Clause, LLMs-from-scratch is Other; Tags unique to speech_recognition: speech-to-text, python, audio, speech-recognition; Also covers AI Agents. ### When should I avoid LLMs-from-scratch? - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience. ### When should I avoid speech_recognition? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is LLMs-from-scratch or speech_recognition more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 8,971). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMs-from-scratch and speech_recognition open source? Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, speech_recognition: BSD-3-Clause). ### Where can I find alternatives to LLMs-from-scratch or speech_recognition? GraphCanon lists graph-backed alternatives at [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) and [speech_recognition alternatives](/tools/uberi-speech-recognition/alternatives) ([LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md), [speech_recognition markdown twin](/tools/uberi-speech-recognition/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/rasbt-llms-from-scratch-vs-uberi-speech-recognition.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMs-from-scratch or speech_recognition? LLMs-from-scratch: Steady. speech_recognition: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for LLMs-from-scratch and speech_recognition? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust); [speech_recognition trust report](/tools/uberi-speech-recognition/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rasbt-llms-from-scratch`](/api/graphcanon/graph?tool=rasbt-llms-from-scratch) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_