---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
- HuggingFaceFW/fineweb-2
- amphion/Emilia-Dataset
- facebook/voxpopuli
- uhhlt/Tuda-De
- openslr/librispeech_asr
- facebook/multilingual_librispeech
- Thorsten-Voice/TV-44kHz-Full
- CSTR-Edinburgh/vctk
- commonvoice_23
- kerstin
language:
- de
- en
base_model:
- utter-project/EuroLLM-1.7B
pipeline_tag: text-to-speech
---
## educa AI voice (preview)
---
educa AI voice is our in-house text to speech model developed on top of [EuroLLM-1.7B](https://huggingface.co/utter-project/EuroLLM-1.7B).
This version of the model is trained on a single speaker and is capable of generating natural-sounding German (and to some extent also English) speech.
Be advised that this is a preview model meant to showcase the base model's capability. We are going to publish more advanced models in the near future (see bottom of this model card).
#### Examples:
### Model details
- **Base LLM**: [EuroLLM-1.7B](https://huggingface.co/utter-project/EuroLLM-1.7B)
- **Audio Tokenizer**: [NeuCodec](https://huggingface.co/neuphonic/neucodec)
#### Pre-training
We pre-trained the model in two stages, first training on billions of tokens of mixed speech and text data using a next-token-prediction objective.
Then, we trained on tens of thousands of hours of German and English TTS data mixed with a little text instruction data to preserve the text understanding capability of the model.
We used the following datasets, as well as some in-house datasets:
- [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)
- [amphion/Emilia-Dataset](https://huggingface.co/datasets/amphion/Emilia-Dataset) (German and English YODAS subsets)
- [facebook/voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli)
- [uhhlt/Tuda-De](https://huggingface.co/datasets/uhhlt/Tuda-De)
- [openslr/librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr)
- [facebook/multilingual_librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech)
- [Thorsten-Voice/TV-44kHz-Full](https://huggingface.co/datasets/Thorsten-Voice/TV-44kHz-Full)
- [CSTR-Edinburgh/vctk](https://huggingface.co/datasets/CSTR-Edinburgh/vctk)
- [commonvoice_23](https://datacollective.mozillafoundation.org/datasets?q=common+voice)
- [kerstin](https://datacollective.mozillafoundation.org/datasets/cmi7mgbam000bnx074097g2yg)
### Inference example
```python
import torch
import torchaudio
from transformers import AutoModelForCausalLM, AutoTokenizer
from neucodec import NeuCodec
device = "cuda"
model_id = "DigitalLearningGmbH/educa-ai-voice-preview"
audio_end_token_id = 128001
audio_tokens_offset = 128006
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
model = model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
codec_model = NeuCodec.from_pretrained("neuphonic/neucodec")
codec_model = codec_model.eval().to(device)
prompt_template = "<|task_tts|>{prompt} <|audio_start|>"
prompt = "Brautkleid bleibt Brautkleid und Blaukraut bleibt Blaukraut."
input_ids = tokenizer.encode(prompt_template.format(prompt=prompt), return_tensors="pt").to(device)
outputs = model.generate(input_ids=input_ids, do_sample=True, temperature=0.6, top_p=0.999, repetition_penalty=1.1, max_new_tokens=2048)
outputs_audio = outputs[0][input_ids.shape[1]:(outputs[0] == audio_end_token_id).nonzero(as_tuple=True)[0][0].item()] - audio_tokens_offset
with torch.no_grad():
recon = codec_model.decode_code(outputs_audio.unsqueeze(0).unsqueeze(0).to(device)).cpu()
torchaudio.save("tts.wav", recon[0, :, :], 24_000)
```
For even higher fidelity in German speech, use our [finetuned NeuCodec decoder](https://huggingface.co/DigitalLearningGmbH/neucodec-decoder-ft-de).
### What's to come
As stated in the model's name, this is a preview model, mainly meant to showcase the capability of the base model.
We trained on a small dataset of a single speaker without any special emotion tagging etc.
We are actively working on
- multiple speakers with emotional control and nonverbal elements (fillers, laughing, ...)
- fine-tuning for general zero-shot voice cloning
- phoneme-based / hybrid generation
- post-training with reinforcement learning
Stay tuned - january 2026 is going to be exciting!
## educa AI voice (preview)
---
educa AI voice is our in-house text to speech model developed on top of [EuroLLM-1.7B](https://huggingface.co/utter-project/EuroLLM-1.7B).
This version of the model is trained on a single speaker and is capable of generating natural-sounding German (and to some extent also English) speech.
Be advised that this is a preview model meant to showcase the base model's capability. We are going to publish more advanced models in the near future (see bottom of this model card).
#### Examples:
### Model details
- **Base LLM**: [EuroLLM-1.7B](https://huggingface.co/utter-project/EuroLLM-1.7B)
- **Audio Tokenizer**: [NeuCodec](https://huggingface.co/neuphonic/neucodec)
#### Pre-training
We pre-trained the model in two stages, first training on billions of tokens of mixed speech and text data using a next-token-prediction objective.
Then, we trained on tens of thousands of hours of German and English TTS data mixed with a little text instruction data to preserve the text understanding capability of the model.
We used the following datasets, as well as some in-house datasets:
- [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
- [HuggingFaceFW/fineweb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)
- [amphion/Emilia-Dataset](https://huggingface.co/datasets/amphion/Emilia-Dataset) (German and English YODAS subsets)
- [facebook/voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli)
- [uhhlt/Tuda-De](https://huggingface.co/datasets/uhhlt/Tuda-De)
- [openslr/librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr)
- [facebook/multilingual_librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech)
- [Thorsten-Voice/TV-44kHz-Full](https://huggingface.co/datasets/Thorsten-Voice/TV-44kHz-Full)
- [CSTR-Edinburgh/vctk](https://huggingface.co/datasets/CSTR-Edinburgh/vctk)
- [commonvoice_23](https://datacollective.mozillafoundation.org/datasets?q=common+voice)
- [kerstin](https://datacollective.mozillafoundation.org/datasets/cmi7mgbam000bnx074097g2yg)
### Inference example
```python
import torch
import torchaudio
from transformers import AutoModelForCausalLM, AutoTokenizer
from neucodec import NeuCodec
device = "cuda"
model_id = "DigitalLearningGmbH/educa-ai-voice-preview"
audio_end_token_id = 128001
audio_tokens_offset = 128006
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
model = model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
codec_model = NeuCodec.from_pretrained("neuphonic/neucodec")
codec_model = codec_model.eval().to(device)
prompt_template = "<|task_tts|>{prompt} <|audio_start|>"
prompt = "Brautkleid bleibt Brautkleid und Blaukraut bleibt Blaukraut."
input_ids = tokenizer.encode(prompt_template.format(prompt=prompt), return_tensors="pt").to(device)
outputs = model.generate(input_ids=input_ids, do_sample=True, temperature=0.6, top_p=0.999, repetition_penalty=1.1, max_new_tokens=2048)
outputs_audio = outputs[0][input_ids.shape[1]:(outputs[0] == audio_end_token_id).nonzero(as_tuple=True)[0][0].item()] - audio_tokens_offset
with torch.no_grad():
recon = codec_model.decode_code(outputs_audio.unsqueeze(0).unsqueeze(0).to(device)).cpu()
torchaudio.save("tts.wav", recon[0, :, :], 24_000)
```
For even higher fidelity in German speech, use our [finetuned NeuCodec decoder](https://huggingface.co/DigitalLearningGmbH/neucodec-decoder-ft-de).
### What's to come
As stated in the model's name, this is a preview model, mainly meant to showcase the capability of the base model.
We trained on a small dataset of a single speaker without any special emotion tagging etc.
We are actively working on
- multiple speakers with emotional control and nonverbal elements (fillers, laughing, ...)
- fine-tuning for general zero-shot voice cloning
- phoneme-based / hybrid generation
- post-training with reinforcement learning
Stay tuned - january 2026 is going to be exciting!