GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
kawshikbuet17
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
kawshikbuet17
Model Tree
canopylabs/orpheus-3b-0.1-pretrained
Input Modalities
Output Modalities
Supported Functionality
This model was fine-tuned to improve Bengali speech synthesis quality using Orpheus TTS.
canopylabs/orpheus-3b-0.1-pretrainedThis adapter must be loaded together with the original Orpheus base model.
Install Hugging Face Hub CLI if needed:
bash
pip install -U "huggingface_hub>=0.30.0,<1.0.0" hf-xetDownload the adapter:
bash
mkdir -p pretrained_models/bengali-orpheus-tts-adapter hf download kawshikbuet17/bengali-orpheus-tts-adapter \ --repo-type model \ --local-dir pretrained_models/bengali-orpheus-tts-adapterVerify the downloaded files:
bash
ls -lah pretrained_models/bengali-orpheus-tts-adapterInstall the required packages. All the packages with versions are mentioned in requirements_finetune.txt file.
If the base Orpheus model requires authentication in your environment, login first:
bash
hf auth loginThis repository contains only the adapter. To use it, first load the base Orpheus model, then load this adapter.
python
import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel base_model_id = "canopylabs/orpheus-3b-0.1-pretrained"adapter_model_id = "kawshikbuet17/bengali-orpheus-tts-adapter" compute_dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16 tokenizer = AutoTokenizer.from_pretrained(base_model_id) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto",) model = PeftModel.from_pretrained( base_model, adapter_model_id,) model.eval()Create a file named infer_bengali_orpheus_tts.py:
python
import sysimport torchimport soundfile as sf from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModelfrom snac import SNAC BASE_MODEL = "canopylabs/orpheus-3b-0.1-pretrained"DEFAULT_ADAPTER = "kawshikbuet17/bengali-orpheus-tts-adapter" TOKENIZER_LENGTH = 128256 END_OF_TEXT = 128009 START_OF_SPEECH = TOKENIZER_LENGTH + 1END_OF_SPEECH = TOKENIZER_LENGTH + 2 START_OF_HUMAN = TOKENIZER_LENGTH + 3END_OF_HUMAN = TOKENIZER_LENGTH + 4 START_OF_AI = TOKENIZER_LENGTH + 5END_OF_AI = TOKENIZER_LENGTH + 6 PAD_TOKEN = TOKENIZER_LENGTH + 7 AUDIO_TOKEN_START = TOKENIZER_LENGTH + 10AUDIO_TOKEN_END = AUDIO_TOKEN_START + 7 * 4096 SAMPLE_RATE = 24000 def build_prompt_ids(tokenizer, text: str): text_ids = tokenizer.encode(text, add_special_tokens=True) text_ids.append(END_OF_TEXT) return ( [START_OF_HUMAN] + text_ids + [END_OF_HUMAN] + [START_OF_AI] + [START_OF_SPEECH] ) def extract_audio_tokens(generated_ids): audio_tokens = [] for token in generated_ids: token = int(token) if token in [END_OF_SPEECH, END_OF_AI, PAD_TOKEN]: break if AUDIO_TOKEN_START <= token < AUDIO_TOKEN_END: audio_tokens.append(token) usable_len = (len(audio_tokens) // 7) * 7 return audio_tokens[:usable_len] def audio_tokens_to_waveform(snac_model, audio_tokens, device): if len(audio_tokens) == 0: raise RuntimeError("No audio tokens generated.") if len(audio_tokens) % 7 != 0: raise RuntimeError(f"Audio token length must be divisible by 7, got {len(audio_tokens)}") n_frames = len(audio_tokens) // 7 codes_0 = [] codes_1 = [] codes_2 = [] for i in range(n_frames): t = audio_tokens[i * 7 : (i + 1) * 7] c0 = t[0] - AUDIO_TOKEN_START c1_0 = t[1] - AUDIO_TOKEN_START - 4096 c2_0 = t[2] - AUDIO_TOKEN_START - 2 * 4096 c2_1 = t[3] - AUDIO_TOKEN_START - 3 * 4096 c1_1 = t[4] - AUDIO_TOKEN_START - 4 * 4096 c2_2 = t[5] - AUDIO_TOKEN_START - 5 * 4096 c2_3 = t[6] - AUDIO_TOKEN_START - 6 * 4096 vals = [c0, c1_0, c2_0, c2_1, c1_1, c2_2, c2_3] if any(v < 0 or v >= 4096 for v in vals): continue codes_0.append(c0) codes_1.extend([c1_0, c1_1]) codes_2.extend([c2_0, c2_1, c2_2, c2_3]) if not codes_0: raise RuntimeError("No valid SNAC frames found from generated tokens.") codes = [ torch.tensor([codes_0], dtype=torch.long, device=device), torch.tensor([codes_1], dtype=torch.long, device=device), torch.tensor([codes_2], dtype=torch.long, device=device), ] with torch.inference_mode(): audio = snac_model.decode(codes) return audio.detach().squeeze().float().cpu().numpy() def main(): adapter_path = sys.argv[1] if len(sys.argv) >= 2 else DEFAULT_ADAPTER prompt = ( sys.argv[2] if len(sys.argv) >= 3 else "আপনার অ্যাকাউন্টের লাস্ট রিচার্জ কত ছিল, বলতে পারবেন?" ) out_path = sys.argv[3] if len(sys.argv) >= 4 else "bengali_orpheus_tts.wav" device = "cuda" if torch.cuda.is_available() else "cpu" compute_dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16 print("Base model:", BASE_MODEL) print("Adapter:", adapter_path) print("Prompt:", prompt) print("Output:", out_path) print("Device:", device) print("Compute dtype:", compute_dtype) tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype, ) print("Loading base model in 4-bit...") base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", ) print("Loading QLoRA adapter...") model = PeftModel.from_pretrained(base_model, adapter_path) model.eval() print("Loading SNAC decoder...") snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(device) snac_model.eval() input_ids_list = build_prompt_ids(tokenizer, prompt) input_ids = torch.tensor([input_ids_list], dtype=torch.long, device=device) attention_mask = torch.ones_like(input_ids) print("Generating speech tokens...") with torch.inference_mode(): output = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=2048, do_sample=True, temperature=0.6, top_p=0.95, repetition_penalty=1.1, eos_token_id=END_OF_SPEECH, pad_token_id=PAD_TOKEN, ) generated_new_tokens = output[0][input_ids.shape[1]:].tolist() audio_tokens = extract_audio_tokens(generated_new_tokens) print("Generated new tokens:", len(generated_new_tokens)) print("Audio tokens:", len(audio_tokens)) print("Audio frames:", len(audio_tokens) // 7) audio = audio_tokens_to_waveform(snac_model, audio_tokens, device) sf.write(out_path, audio, SAMPLE_RATE) print("Saved:", out_path) if __name__ == "__main__": main()Run inference with the public adapter repo:
bash
python infer_bengali_orpheus_tts.py \ kawshikbuet17/bengali-orpheus-tts-adapter \ "আপনার অ্যাকাউন্টের লাস্ট রিচার্জ কত ছিল, বলতে পারবেন?" \ output.wavOr run with default values:
bash
python infer_bengali_orpheus_tts.pyThis model was fine-tuned using a Bengali telecom/customer-care speech dataset.
Training dataset:
kawshikbuet17/bengali-telecom-customer-care-speech
The dataset contains Bengali customer-care style speech and text examples.
The adapter was trained using QLoRA-style fine-tuning.
Main training configuration:
yaml
base_model: canopylabs/orpheus-3b-0.1-pretrainedlanguage: Bengali / Banglatask: Text-to-Speechadapter_type: QLoRA / LoRAlora_r: 16lora_alpha: 32lora_dropout: 0.05This model generates synthetic speech.
Generated audio should be treated as synthetic. Do not use this model to impersonate a real person, clone voices without permission, or create misleading audio.
This model is intended for:
Do not use this model to:
canopylabs/orpheus-3b-0.1-pretrained for inference.This model is fine-tuned from Orpheus TTS.
Please also acknowledge the original Orpheus TTS project when using this model.
Kawshik Kumar Paul
Dept. of CSE, BUET
Email: kawshikbuet17@gmail.com
mkdir -p pretrained_models/bengali-orpheus-tts-adapter hf download kawshikbuet17/bengali-orpheus-tts-adapter \ --repo-type model \ --local-dir pretrained_models/bengali-orpheus-tts-adapterimport torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel base_model_id = "canopylabs/orpheus-3b-0.1-pretrained"adapter_model_id = "kawshikbuet17/bengali-orpheus-tts-adapter" compute_dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16 tokenizer = AutoTokenizer.from_pretrained(base_model_id) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto",) model = PeftModel.from_pretrained( base_model, adapter_model_id,) model.eval()import sysimport torchimport soundfile as sf from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModelfrom snac import SNAC BASE_MODEL = "canopylabs/orpheus-3b-0.1-pretrained"DEFAULT_ADAPTER = "kawshikbuet17/bengali-orpheus-tts-adapter" TOKENIZER_LENGTH = 128256 END_OF_TEXT = 128009 START_OF_SPEECH = TOKENIZER_LENGTH + 1END_OF_SPEECH = TOKENIZER_LENGTH + 2 START_OF_HUMAN = TOKENIZER_LENGTH + 3END_OF_HUMAN = TOKENIZER_LENGTH + 4 START_OF_AI = TOKENIZER_LENGTH + 5END_OF_AI = TOKENIZER_LENGTH + 6 PAD_TOKEN = TOKENIZER_LENGTH + 7 AUDIO_TOKEN_START = TOKENIZER_LENGTH + 10AUDIO_TOKEN_END = AUDIO_TOKEN_START + 7 * 4096 SAMPLE_RATE = 24000 def build_prompt_ids(tokenizer, text: str): text_ids = tokenizer.encode(text, add_special_tokens=True) text_ids.append(END_OF_TEXT) return ( [START_OF_HUMAN] + text_ids + [END_OF_HUMAN] + [START_OF_AI] + [START_OF_SPEECH] ) def extract_audio_tokens(generated_ids): audio_tokens = [] for token in generated_ids: token = int(token) if token in [END_OF_SPEECH, END_OF_AI, PAD_TOKEN]: break if AUDIO_TOKEN_START <= token < AUDIO_TOKEN_END: audio_tokens.append(token) usable_len = (len(audio_tokens) // 7) * 7 return audio_tokens[:usable_len] def audio_tokens_to_waveform(snac_model, audio_tokens, device): if len(audio_tokens) == 0: raise RuntimeError("No audio tokens generated.") if len(audio_tokens) % 7 != 0: raise RuntimeError(f"Audio token length must be divisible by 7, got {len(audio_tokens)}") n_frames = len(audio_tokens) // 7 codes_0 = [] codes_1 = [] codes_2 = [] for i in range(n_frames): t = audio_tokens[i * 7 : (i + 1) * 7] c0 = t[0] - AUDIO_TOKEN_START c1_0 = t[1] - AUDIO_TOKEN_START - 4096 c2_0 = t[2] - AUDIO_TOKEN_START - 2 * 4096 c2_1 = t[3] - AUDIO_TOKEN_START - 3 * 4096 c1_1 = t[4] - AUDIO_TOKEN_START - 4 * 4096 c2_2 = t[5] - AUDIO_TOKEN_START - 5 * 4096 c2_3 = t[6] - AUDIO_TOKEN_START - 6 * 4096 vals = [c0, c1_0, c2_0, c2_1, c1_1, c2_2, c2_3] if any(v < 0 or v >= 4096 for v in vals): continue codes_0.append(c0) codes_1.extend([c1_0, c1_1]) codes_2.extend([c2_0, c2_1, c2_2, c2_3]) if not codes_0: raise RuntimeError("No valid SNAC frames found from generated tokens.") codes = [ torch.tensor([codes_0], dtype=torch.long, device=device), torch.tensor([codes_1], dtype=torch.long, device=device), torch.tensor([codes_2], dtype=torch.long, device=device), ] with torch.inference_mode(): audio = snac_model.decode(codes) return audio.detach().squeeze().float().cpu().numpy() def main(): adapter_path = sys.argv[1] if len(sys.argv) >= 2 else DEFAULT_ADAPTER prompt = ( sys.argv[2] if len(sys.argv) >= 3 else "আপনার অ্যাকাউন্টের লাস্ট রিচার্জ কত ছিল, বলতে পারবেন?" ) out_path = sys.argv[3] if len(sys.argv) >= 4 else "bengali_orpheus_tts.wav" device = "cuda" if torch.cuda.is_available() else "cpu" compute_dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16 print("Base model:", BASE_MODEL) print("Adapter:", adapter_path) print("Prompt:", prompt) print("Output:", out_path) print("Device:", device) print("Compute dtype:", compute_dtype) tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=compute_dtype, ) print("Loading base model in 4-bit...") base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", ) print("Loading QLoRA adapter...") model = PeftModel.from_pretrained(base_model, adapter_path) model.eval() print("Loading SNAC decoder...") snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to(device) snac_model.eval() input_ids_list = build_prompt_ids(tokenizer, prompt) input_ids = torch.tensor([input_ids_list], dtype=torch.long, device=device) attention_mask = torch.ones_like(input_ids) print("Generating speech tokens...") with torch.inference_mode(): output = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=2048, do_sample=True, temperature=0.6, top_p=0.95, repetition_penalty=1.1, eos_token_id=END_OF_SPEECH, pad_token_id=PAD_TOKEN, ) generated_new_tokens = output[0][input_ids.shape[1]:].tolist() audio_tokens = extract_audio_tokens(generated_new_tokens) print("Generated new tokens:", len(generated_new_tokens)) print("Audio tokens:", len(audio_tokens)) print("Audio frames:", len(audio_tokens) // 7) audio = audio_tokens_to_waveform(snac_model, audio_tokens, device) sf.write(out_path, audio, SAMPLE_RATE) print("Saved:", out_path) if __name__ == "__main__": main()python infer_bengali_orpheus_tts.py \ kawshikbuet17/bengali-orpheus-tts-adapter \ "আপনার অ্যাকাউন্টের লাস্ট রিচার্জ কত ছিল, বলতে পারবেন?" \ output.wavbase_model: canopylabs/orpheus-3b-0.1-pretrainedlanguage: Bengali / Banglatask: Text-to-Speechadapter_type: QLoRA / LoRAlora_r: 16lora_alpha: 32lora_dropout: 0.05