kyutai
@kyutai_labs · Paris, France · joined 12 Nov 2023
27 690Followers
14Following
243Posts total
531.5KViews on collected posts
Últimas publicaciones
Buscando publicaciones nuevas…
It should be soon possible to target CPUs, GPUs, TPUs, NPUs and ASICs with a single PyTorch installation, without performance impact! Gabriel de Marmiesse presented his work at Kyutai on integrating Mojo into PyTorch at ModCon last week. Links below https://t.co/0azy44q9EJ
6.5K views · 107 likes
· 12 reposts · 5 replies
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@kyutai_labs PocketTTS --- the fastest model in the audio.cpp Arena! https://t.co/ufiRgHnPj1
397 views · 3 likes
· 0 reposts · 0 replies
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@kyutai_labs Love Pocket, but can you train the English model to say "live" with a hard "i" when appropriate? In my experience, it incorrectly says it with a soft 'i' 90% of the time.
555 views · 2 likes
· 0 reposts · 0 replies
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@kyutai_labs Thanks for releasing the training code it matches what i was doing. only issue was mine kept blabbering and eos token prediction had issues
638 views · 3 likes
· 1 reposts · 1 replies
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If you're looking for a weekend project, how about training your own text-to-speech model from scratch on your own GPU, and then running it on any device's CPU?
We just open-sourced the entire Pocket TTS training stack: data pipeline, recipes, and evals.
It learns pretty damn h
104.4K views · 1K likes
· 118 reposts · 52 replies
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MuScriptor, our music transcription model, can now make sheet music and tabs! We export PDFs and editable MusicXML.
Under the hood, we generate these from MIDI using @musescore + some custom preprocessing. https://t.co/zWHqbR0zmJ
37.3K views · 468 likes
· 53 reposts · 21 replies
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Our audio-to-MIDI model, MuScriptor, now also detects tempo! You can directly drag-and-drop the MIDI into a DAW and it just works, without you having to tempo-match manually. Have fun! https://t.co/zbBNtkknIK
27.6K views · 346 likes
· 31 reposts · 8 replies
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@kyutai_labs @MireloAI Cool, you can just do things now 🤷 https://t.co/E77u6YKzEu
15K views · 63 likes
· 3 reposts · 3 replies
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Transcription has many creative uses, from learning an instrument to music production. We’re excited to see what musicians can do with MuScriptor!
Start transcribing: https://t.co/KoPtirqti8
Code: https://t.co/FKFNCoqBe4
Paper: https://t.co/wGGUcxjaN0
10K views · 125 likes
· 8 reposts · 7 replies
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MuScriptor is a decoder-only transformer that autoregressively predicts a stream of MT3-like tokens from a mel-spectrogram of a 5-second audio excerpt. Longer samples are transcribed chunk-by-chunk. The model sizes span from 100M all the way to 1.3B parameters.
7.2K views · 32 likes
· 0 reposts · 1 replies
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We pre-train on synthetic data: 1.5 million MIDI files, synthesized in many variations. Then we fine-tune on real data, the single biggest driver of quality. Finally, we post-train using RL on 300 tracks with manually verified accurate transcriptions.
8.3K views · 38 likes
· 0 reposts · 1 replies
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The main improvement comes from data, the lack of which has bottlenecked automatic music transcription since MT3 (2022). We collected a dataset of 170k music recordings (11k hours), paired with MIDI transcriptions and spanning many genres.
7.9K views · 34 likes
· 0 reposts · 1 replies
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We're releasing MuScriptor, the best open model for multi-instrument transcription to date, created in collaboration with @MireloAI.
Give it a recording in any genre: pop, classical, metal, jazz, whatever, and it transcribes the individual instruments into MIDI. Link in 🧵 https:/
305.6K views · 1.3K likes
· 202 reposts · 38 replies
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Frente a cuentas del mismo tamaño
13 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. llega a mucha gente, pero pocos de esos espectadores reaccionan.
Visualizaciones medianas8 289esta cuenta924mediana de 10K–100K
Alcance, %29.93%esta cuenta3.62%mediana de 10K–100K
Interacción, %0.76%esta cuenta1.52%mediana de 10K–100K
| Métrica | Esta cuenta | Mediana de 10K–100K | Proporción |
|---|---|---|---|
| Visualizaciones medianas por publicación | 8 289 | 924 | 8.97× |
| Alcance (visualizaciones ÷ seguidores) | 29.93% | 3.62% | 8.27× |
| Tasa de interacción | 0.76% | 1.52% | 0.50× |
Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
305.6K10 Jul
7.9K
8.3K
7.2K
10K
15K11 Jul
27.6K6 Aug
37.3K20 Aug
104.4K25 Aug
638
555
39726 Aug
6.5K
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.52%10 Jul
0.45%
0.47%
0.46%
1.41%
0.49%11 Jul
1.41%6 Aug
1.48%20 Aug
1.17%25 Aug
0.78%
0.36%
0.76%26 Aug
1.93%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes46.2%3 551 in total
Reposts5.6%428 in total
Replies1.8%138 in total
Quotes1.2%89 in total
Bookmarks45.3%3 486 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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