Zum Inhalt springen
ModelAufgelistetExtern
Browse files

Bananamind Tts GGUF

GGUF quantized versions of Banaxi-Tech/BananaMind-TTS-V2.1-Preview for use with CrispASR.

ArtOtherBasismodellBanaxi-Tech/BananaMind-TTS-V2.1-PreviewVersionRevision 88ae3aaBBewertung

Modellquelle

Art
Other
Dateigröße
38,3–49,8 MB
Basismodell
Banaxi-Tech/BananaMind-TTS-V2.1-Preview
Version
vRevision 88ae3aa
Aktualisiert
6. Okt. 2026
Downloads
817
Quelle
Hugging Face

Quellenbeschreibung

BananaMind-TTS-V2.1 — GGUF

GGUF quantized versions of Banaxi-Tech/BananaMind-TTS-V2.1-Preview for use with CrispASR.

Model overview

BananaMind-TTS-V2.1 is a lightweight Tacotron-lite text-to-speech model (13M params) with a HiFi-GAN vocoder. Character-based tokenizer, 22050 Hz mono output, fixed voice per locale.

Architecture: Embedding → 3× Conv1d+BatchNorm+ReLU → BiLSTM encoder → autoregressive GRU decoder with location-sensitive attention → 5-layer postnet → HiFi-GAN vocoder.

Locales: English (en-us, trained on LJ Speech) and German (de-de, trained on ThorstenVoice).

Files

FileLocaleQuantSize
bananamind-tts-en-q8_0.ggufEnglishQ8_0~39 MB
bananamind-tts-de-q8_0.ggufGermanQ8_0~39 MB
bananamind-tts-en-f32.ggufEnglishF32~50 MB
bananamind-tts-de-f32.ggufGermanF32~50 MB

Q8_0 is recommended for most use cases — negligible quality loss vs F32 at 22% smaller size.

Usage with CrispASR

# English
crispasr -m bananamind-tts-en-q8_0.gguf \
  --tts "This is BananaMind TTS version two point one." \
  --tts-output output.wav

# German
crispasr -m bananamind-tts-de-q8_0.gguf \
  --tts "Hallo, das ist BananaMind TTS Version zwei Punkt eins." \
  --tts-output output_de.wav

Auto-detection works — no --backend flag needed.

Python

import crispasr

session = crispasr.Session("bananamind-tts-en-q8_0.gguf")
pcm = session.synthesize("Hello, world!")
session.save_wav("output.wav", pcm)

Conversion

Converted from the original safetensors using:

python models/convert-bananamind-tts-to-gguf.py \
  --model Banaxi-Tech/BananaMind-TTS-V2.1-Preview \
  --locale en-us --ftype f32 \
  --output bananamind-tts-en-f32.gguf

crispasr-quantize bananamind-tts-en-f32.gguf bananamind-tts-en-q8_0.gguf q8_0

Environment variables

VariableDescription
CRISPASR_BANANAMIND_DEBUG=1Per-step decoder diagnostics (attention index, stop probability, mel range)
BANANAMIND_TTS_BENCH=1Per-stage timing (encoder, ar_decoder, postnet, vocoder)

License

Apache 2.0 — same as the original model.

Credits

  • Model: Banaxi-Tech — BananaMind-TTS-V2.1-Preview
  • English training data: LJ Speech Dataset (Keith Ito & Linda Johnson, 2017)
  • German training data: ThorstenVoice Dataset 2022.10 (CC0, DOI: 10.5281/zenodo.7265581)
  • GGUF conversion & CrispASR backend: CrispASR

Quellen

1 Quelle
Hugging FacePrimärVersion 88ae3aacbf8f27cfae9407d489046784ff2f1301
huggingface.co/cstr/bananamind-tts-GGUF/tree/88ae3aacbf8f27cfae9407d489046784ff2f1301

Verifiziert 23. Aug.

Download files

Modellartefakte

4 Artefakte

bananamind-tts-de-f32.gguf

gguf · 49,8 MB · SHA-256 12000ef8a802…83b3 · Hugging Face

Download file

bananamind-tts-de-q8_0.gguf

gguf · 38,3 MB · SHA-256 ebef4e027076…1754 · Hugging Face

Download file

bananamind-tts-en-f32.gguf

gguf · 49,8 MB · SHA-256 72aa2279b9ba…3aea · Hugging Face

Download file

bananamind-tts-en-q8_0.gguf

gguf · 38,3 MB · SHA-256 37e5da4ff1c9…9565 · Hugging Face

Download file

Quellenauszüge

2 Auszüge

--- license: apache-2.0 base_model: Banaxi-Tech/BananaMind-TTS-V2.1-Preview tags: - text-to-speech - tts - tacotron - hifigan - gguf - crispasr language: - en - de pipeline_tag: text-to-speech --- # BananaMind-TTS-V2.1 — GGUF GGUF quantized versions of [Banaxi-Tech/BananaMind-TTS-V2.1-Preview](https://huggingface.co/Banaxi-Tech/BananaMind-TTS-V2.1-Preview) for use with [CrispASR](https://github.com/CrispStrobe/CrispASR). ## Model overview BananaMind-TTS-V2.1 is a lightweight Tacotron-lite text-to-speech model (13M params) with a HiFi-GAN vocoder. Character-based tokenizer, 22050 Hz mono output, fixed voice per locale. **Architecture:** Embedding → 3× Conv1d+BatchNorm+ReLU → BiLSTM encoder → autoregressive GRU decoder with location-sensitive attention → 5-layer postnet → HiFi-GAN vocoder. **Locales:** English (en-us, trained on LJ Speech) and German (de-de, trained on ThorstenVoice). ## Files | File | Locale | Quant | Size | |------|--------|-------|------| | `bananamind-tts-en-q8_0.gguf` | English | Q8_0 | ~39 MB | | `bananamind-tts-de-q8_0.gguf` | German | Q8_0 | ~39 MB | | `bananamind-tts-en-f32.gguf` | English | F32 | ~50 MB | | `bananamind-tts-de-f32.gguf` | German | F32 | ~50 MB | Q8_0 is recommended for most use cases — negligible quality loss vs F32 at 22% smaller size. ## Usage with CrispASR ```bash # English crispasr -m bananamind-tts-en-q8_0.gguf \ --tts "This is BananaMind TTS version two point one." \ --tts-output output.wav # German crispasr -m bananamind-tts-de-q8_0.gguf \ --tts "Hallo, das ist BananaMind TTS Version zwei Punkt eins." \ --tts-output output_de.wav ``` Auto-detection works — no `--backend` flag needed. ### Python ```python import crispasr session = crispasr.Session("bananamind-tts-en-q8_0.gguf") pcm = session.synthesize("Hello, world!") session.save_wav("output.wav", pcm) ``` ## Conversion Converted from the original safetensors using: ```bash python models/convert-bananamind-tts-to-gguf.py \ --model Banaxi-Tech/BananaMind-TTS-V2.1-Preview \ --locale en-us --ftype f32 \ --output bananamind-tts-en-f32.gguf crispasr-quantize bananamind-tts-en-f32.gguf bananamind-tts-en-q8_0.gguf q8_0 ``` ## Environment variables | Variable | Description | |----------|-------------| | `CRISPASR_BANANAMIND_DEBUG=1` | Per-step decoder diagnostics (attention index, stop probability, mel range) | | `BANANAMIND_TTS...

Source context: 817 downloads · 4 likes · Pipeline text-to-speech · Repo cstr/bananamind-tts-GGUF