The 38 synthetic voice memos used throughout Module 2 (scripts and answer key in memos.json, names.md, transcripts from Grok and a second ASR API)
| File / folder | Description | Size |
|---|---|---|
| ๐ ../ | Up one level | - |
| ๐ audio/ | Subfolder | - |
| ๐ MAPPING.md | 5.5 KB | |
| ๐ README.md | 6.9 KB | |
| ๐ build_memos.py | 12.1 KB | |
| ๐ lines.json | 10.4 KB | |
| ๐ memos.json | 12.2 KB | |
| ๐ name_scores.json | 2.0 KB | |
| ๐ score_names.py | 2.5 KB | |
| ๐ transcribe.py | 2.2 KB | |
| ๐ transcripts_compat.json | 9.5 KB | |
| ๐ transcripts_grok.json | 9.5 KB | |
| ๐ transcripts_grok_terms.json | 9.5 KB | |
| ๐ tts.log | 7.8 KB |
This is the shared input for the English module 2 experiments (2.1โ2.9). It holds 38 short voice memos in natural spoken American English, each 40โ90 words, recorded as if someone were talking into a phone. Every person in them is fictional. The memos mirror the Chinese corpus one to one: the ids are the same, and each memo does the same job its Chinese twin does. See MAPPING.md.
Across the 28 W+L memos there are 31 name mentions and 22 term mentions, the same per-role and per-term counts as the Chinese corpus. "I mean" appears in 11 of the 28 W+L memos and in 4 of the I memos. The scripts have no um/uh, because the ASR strips them anyway.
The memos are read in the author's voice. This is a voice clone the author consented to, made with local Qwen3-TTS (Qwen/Qwen3-TTS-12Hz-1.7B-Base, via tts-clone qwen-clone on Apple Silicon). The clone works across languages: the reference recording is the author speaking Chinese. That recording lives in the private/ directory of the tts_clone_skill project and is not distributed. Synthesis is free and local, and took about 20 minutes for 38 memos (tts.log).
memos.json: the scripts plus about, people (9) and terms (12). It is generated by build_memos.py, which also writes lines.json and prints the per-memo counts. "terminal" is in the terms list (the author's written form, mirroring Apple in the Chinese list) but is never spoken and never scored.lines.json: the TTS input, [{"id", "text"}].audio/*.wav: 38 files, about 9.5 minutes in total.transcripts_*.json: AI Builder Space ASR output, {memo_id: text}, all with language=en:grok: /audio/grok-transcription, no vocabulary.grok_terms: the same, with terms set to the 9 names and the 11 spoken terms.compat: the OpenAI-compatible /audio/transcriptions route (whisper-1).score_names.py โ name_scores.json: name and term accuracy per transcript set.MAPPING.md: Chinese id โ English id, what each memo plants, and which section uses it.# 0. (optional) regenerate memos.json and lines.json from build_memos.py
.venv/bin/python experiments_en/corpus/build_memos.py
# 1. synthesize (needs the local tts-clone and the private reference audio)
tts-clone qwen-clone --ref-audio <reference.wav> --ref-text-file <reference.txt> \
--lines-file experiments_en/corpus/lines.json --out-dir experiments_en/corpus/audio/
# 2. transcribe, three ways (skips any transcripts_*.json that already exists)
op run --env-file experiments/aib.env -- .venv/bin/python experiments_en/corpus/transcribe.py
# 3. score
.venv/bin/python experiments_en/corpus/score_names.py
| ASR | names right | terms right (case-sensitive) |
|---|---|---|
| Grok, no vocabulary | 5/31 | 14/22 (10) |
| OpenAI-compatible route | 8/31 | 20/22 (17) |
Grok, names and terms in terms |
27/31 | 22/22 (20) |
The Chinese baseline was 4/31 names in both raw engines, 19โ20/22 terms, and 21/31 names with terms.
Names.
Names that survive raw ASR.
Terms. Raw grok gets fewer terms right than in Chinese: KoyebโCoyab, vLLMโValM/VLM, LoRAโ"Elora", VoiceFlowโ"voice flow". compat gets 20/22. With terms, grok gets every term (two are uppercase VLLM) and 27/31 names. The four misses are W03 Aoife (the phrase is gone), W07 Saoirse ("Source says"), W12 Kaveh (Kavish) and W20 Niamh (Nave).
Things to know about the transcripts.
Scores after the Niamh โ Xochitl swap (W+L, 31 name mentions): grok 3/31, compat 6/31, grok with terms 28/31. Terms unchanged (grok 14/22, compat 20/22, grok with terms 22/22).