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ablmontazer/
orpheus-australian-en-tts

Name: orpheus-australian-en-tts
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Orpheus Australian-English TTS

A finetune of canopylabs/orpheus-tts-0.1-pretrained (Llama-3.2-3B backbone + SNAC 24 kHz neural codec) that speaks Australian-accented English in 8 named voices. Trained on public-domain Australian LibriVox audiobooks and dramatic readings, with mild DeepFilterNet denoising.

Voices

Pass one of these names as the speaker. (Names are random pseudonyms; the underlying public-domain readers are not identified.)

voice	gender
`flynn`	male
`angus`	male
`mason`	male
`cooper`	male
`delta`	female
`ruby`	female
`sienna`	female
`matilda`	female

Samples

Generated with this model at temperature=0.6. Nothing was hand-picked by ear: each clip was transcribed back with Whisper large-v3-turbo and kept only if it matched the target sentence (31 of 32 first takes passed; one was re-rolled). Browse or download them in samples/, or open the demo Space.

“Good morning! It's a lovely day for a walk down by the harbour, isn't it?”

voice		voice
`flynn` (male)		`angus` (male)
`mason` (male)		`cooper` (male)
`delta` (female)		`ruby` (female)
`sienna` (female)		`matilda` (female)

“The dance class starts at half past three, so we'd rather take the car than walk in this weather.”

voice		voice
`flynn` (male)		`angus` (male)
`mason` (male)		`cooper` (male)
`delta` (female)		`ruby` (female)
`sienna` (female)		`matilda` (female)

“No worries, mate. I'll grab a coffee on the way and meet you at the station this afternoon.”

voice		voice
`flynn` (male)		`angus` (male)
`mason` (male)		`cooper` (male)
`delta` (female)		`ruby` (female)
`sienna` (female)		`matilda` (female)

“It was a warm afternoon in the bush, and the road stretched on for miles ahead of us, past the grey gum trees and the dry creek bed.”

voice		voice
`flynn` (male)		`angus` (male)
`mason` (male)		`cooper` (male)
`delta` (female)		`ruby` (female)
`sienna` (female)		`matilda` (female)

Install

bash

pip install torch transformers soundfile
pip install snac            # neural audio codec used by Orpheus

If transformers fails to import with a Keras-3 error, run with USE_TF=0 USE_FLAX=0.

Quick start / deployment

A single self-contained inference function. Works on a CUDA GPU (bf16 ~7 GB VRAM); for CPU drop .to("cuda") and use float32.

python

import torch, soundfile as sf
from transformers import AutoModelForCausalLM, AutoTokenizer
from snac import SNAC

REPO = "ablmontazer/orpheus-australian-en-tts"
DEV  = "cuda"
# Orpheus control tokens (fixed)
SOH, EOT, EOH, SOAI, SOS, EOS, EOAI, PAD, BASE_A = 128259,128009,128260,128261,128257,128258,128262,128263,128266

tok   = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype=torch.bfloat16).to(DEV).eval()
snac  = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(DEV).eval()

@torch.inference_mode()
def tts(voice: str, text: str, out_path: str = "out.wav", temperature: float = 0.6):
    # prompt format is  "<voice>: <text>"
    ids = [SOH] + tok.encode(f"{voice}: {text}", add_special_tokens=False) + [EOT, EOH, SOAI, SOS]
    gen = model.generate(torch.tensor([ids]).to(DEV), max_new_tokens=1400, do_sample=True,
                         temperature=temperature, top_p=0.95, eos_token_id=EOAI, pad_token_id=PAD)[0].tolist()
    audio = [t for t in gen[len(ids):] if BASE_A <= t < BASE_A + 7*4096]
    a = [t - BASE_A for t in audio]; n = len(a)//7
    c0, c1, c2 = [], [], []
    for i in range(n):                      # de-interleave 7 tokens/frame -> 3 SNAC codebooks
        f = a[i*7:i*7+7]
        c0.append(f[0]%4096)
        c1.append(f[1]%4096); c1.append(f[4]%4096)
        c2.append(f[2]%4096); c2.append(f[3]%4096); c2.append(f[5]%4096); c2.append(f[6]%4096)
    codes = [torch.tensor(c0)[None].to(DEV), torch.tensor(c1)[None].to(DEV), torch.tensor(c2)[None].to(DEV)]
    y = snac.decode(codes)[0, 0].cpu().numpy()
    sf.write(out_path, y, 24000)
    return out_path

# examples
tts("delta",  "Good morning! It's a lovely day for a walk down by the harbour.")
tts("flynn",  "No worries mate, I'll give you a hand with that.")

Available voices: flynn, angus, mason, cooper, delta, ruby, sienna, matilda.

Minimal HTTP server (optional)

python

from fastapi import FastAPI
from fastapi.responses import FileResponse
app = FastAPI()

@app.get("/tts")
def synth(voice: str, text: str):
    return FileResponse(tts(voice, text, "/tmp/tts.wav"), media_type="audio/wav")
# uvicorn app:app --host 0.0.0.0 --port 8000

Notes

Sampling: temperature 0.5-0.7 is a good range; lower = steadier, higher = more expressive.
Output is 24 kHz mono.
One voice (the smallest) has less training data and is slightly less stable.

Training

~22 h total across the 8 voices (capped ~4 h/voice), 24 kHz, mild DeepFilterNet denoise.
Sequence: [SOH] + text_ids("voice: text") + [EOT,EOH,SOAI,SOS] + snac_audio + [EOS,EOAI].
Eval + early stopping (best checkpoint kept), cosine LR 5e-5, bf16, ~1 epoch effective.

Data & license

Source audio is public domain (LibriVox). Base model and code under Apache-2.0. Speaker identities are anonymised (pseudonymous voice names).