import spaces from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch import librosa import gradio as gr from snac import SNAC import re orpheus_model_id = 'NandemoGHS/Galgame-Orpheus-3B' tokenizer = AutoTokenizer.from_pretrained(orpheus_model_id) model = AutoModelForCausalLM.from_pretrained( orpheus_model_id, torch_dtype=torch.bfloat16, trust_remote_code=True, ) model.eval().cuda() snac_model_id = 'hubertsiuzdak/snac_24khz' snac_model = SNAC.from_pretrained(snac_model_id) snac_model.eval().cuda() whisper_turbo_pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-large-v3-turbo", torch_dtype=torch.float16, device='cuda', ) SOT_ID = 128000 # Start of Text (Not used) EOT_ID = 128009 # End of Text SOS_ID = 128257 # Start of Speech EOS_ID = 128258 # End of Speech SOH_ID = 128259 # Start of Human EOH_ID = 128260 # End of Human SOA_ID = 128261 # Start of AI EOA_ID = 128262 # End of AI REPLACE_MAP: dict[str, str] = { r"\t": "", r"\[n\]": "", r" ": "", r"　": "", r"[;▼♀♂《》≪≫①②③④⑤⑥]": "", r"[\u02d7\u2010-\u2015\u2043\u2212\u23af\u23e4\u2500\u2501\u2e3a\u2e3b]": "", r"[\uff5e\u301C]": "ー", r"？": "?", r"！": "!", r"[●◯〇]": "○", r"♥": "♡", } FULLWIDTH_ALPHA_TO_HALFWIDTH = str.maketrans( { chr(full): chr(half) for full, half in zip( list(range(0xFF21, 0xFF3B)) + list(range(0xFF41, 0xFF5B)), list(range(0x41, 0x5B)) + list(range(0x61, 0x7B)), ) } ) HALFWIDTH_KATAKANA_TO_FULLWIDTH = str.maketrans( { chr(half): chr(full) for half, full in zip(range(0xFF61, 0xFF9F), range(0x30A1, 0x30FB)) } ) FULLWIDTH_DIGITS_TO_HALFWIDTH = str.maketrans( { chr(full): chr(half) for full, half in zip(range(0xFF10, 0xFF1A), range(0x30, 0x3A)) } ) INVALID_PATTERN = re.compile( r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005" r"\u0041-\u005A\u0061-\u007A" r"\u0030-\u0039" r"。、!?…♪♡○]" ) def normalize(text: str) -> str: for pattern, replacement in REPLACE_MAP.items(): text = re.sub(pattern, replacement, text) text = text.translate(FULLWIDTH_ALPHA_TO_HALFWIDTH) text = text.translate(FULLWIDTH_DIGITS_TO_HALFWIDTH) text = text.translate(HALFWIDTH_KATAKANA_TO_FULLWIDTH) text = re.sub(r"…{3,}", "……", text) def replace_special_chars(match): seq = match.group(0) return seq[0] if len(set(seq)) == 1 else seq[0] + seq[-1] return text def tokenize_audio(waveform): waveform = waveform.unsqueeze(0) with torch.inference_mode(): codes = snac_model.encode(waveform) all_codes = [] for i in range(codes[0].shape[1]): all_codes.append(codes[0][0][i].item()+128266) all_codes.append(codes[1][0][2*i].item()+128266+4096) all_codes.append(codes[2][0][4*i].item()+128266+(2*4096)) all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096)) all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096)) all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096)) all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096)) return all_codes def redistribute_codes(code_list): new_length = (len(code_list) // 7) * 7 if new_length == 0: return None code_list = code_list[:new_length] layer_1 = [] layer_2 = [] layer_3 = [] for i in range((len(code_list)+1)//7): layer_1.append(code_list[7*i]) layer_2.append(code_list[7*i+1]-4096) layer_3.append(code_list[7*i+2]-(2*4096)) layer_3.append(code_list[7*i+3]-(3*4096)) layer_2.append(code_list[7*i+4]-(4*4096)) layer_3.append(code_list[7*i+5]-(5*4096)) layer_3.append(code_list[7*i+6]-(6*4096)) codes = [ torch.tensor(layer_1).unsqueeze(0), torch.tensor(layer_2).unsqueeze(0), torch.tensor(layer_3).unsqueeze(0) ] print(codes) codes = [c.cuda() for c in codes] with torch.no_grad(): audio_hat = snac_model.decode(codes) return audio_hat @spaces.GPU(duration=60) def infer(sample_audio_path, target_text, temperature, top_p, repetition_penalty, progress=gr.Progress()): if not target_text or not target_text.strip(): gr.Warning("Please input text to generate audio.") return None, None if len(target_text) > 300: gr.Warning("Text is too long. Please keep it under 300 characters.") target_text = target_text[:300] target_text = normalize(target_text) with torch.no_grad(): if sample_audio_path: progress(0, 'Loading and trimming audio...') audio_array, sample_rate = librosa.load(sample_audio_path, sr=24000) if len(audio_array) / sample_rate > 15: gr.Warning("Trimming audio to first 15secs.") num_samples_to_keep = int(sample_rate * 15) audio_array = audio_array[:num_samples_to_keep] prompt_wav = torch.from_numpy(audio_array).unsqueeze(0) prompt_wav = prompt_wav.to(dtype=torch.float32) progress(0.2, 'Transcribing reference audio...') prompt_text = whisper_turbo_pipe(prompt_wav[0].numpy())['text'].strip() progress(0.4, 'Transcribed! Encoding audio...') # Encode the prompt wav snac_dev = next(snac_model.parameters()).device voice_tokens = tokenize_audio(prompt_wav.to(device=snac_dev)) ref_text_ids = tokenizer(prompt_text, return_tensors="pt").input_ids[0].tolist() prompt_ids = ( [SOH_ID] + ref_text_ids + [EOT_ID] + [EOH_ID] + [SOA_ID] + [SOS_ID] + voice_tokens + [EOS_ID] + [EOA_ID] ) else: prompt_ids = [] progress(0.6, "Generating audio...") target_text_ids = tokenizer(target_text, return_tensors="pt").input_ids[0].tolist() prompt_ids.extend([SOH_ID]) prompt_ids.extend(target_text_ids) prompt_ids.extend([EOT_ID]) prompt_ids.extend([EOH_ID]) prompt_ids.extend([SOA_ID]) prompt_ids.extend([SOS_ID]) print(prompt_ids) input_ids = torch.tensor([prompt_ids], dtype=torch.int64).cuda() # Generate the speech autoregressively outputs = model.generate( input_ids, max_new_tokens=2048, eos_token_id=EOS_ID, do_sample=True, top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty, ) generated_ids = outputs[0].tolist() print(generated_ids) progress(0.8, "Decoding generated audio...") try: last_sos_idx = len(generated_ids) - 1 - generated_ids[::-1].index(SOS_ID) speech_tokens = generated_ids[last_sos_idx + 1:] except ValueError: gr.Error("Audio generation failed: Could not find end of header token.") return None, None if EOS_ID in speech_tokens: speech_tokens = speech_tokens[:speech_tokens.index(EOS_ID)] if not speech_tokens: gr.Error("Audio generation failed: No speech tokens were generated.") return None, None base_offset = 128266 adjusted_tokens = [token - base_offset for token in speech_tokens if token >= base_offset] gen_wav_tensor = redistribute_codes(adjusted_tokens) if gen_wav_tensor is None: gr.Error("Audio decoding failed.") return None, None gen_wav = gen_wav_tensor.cpu().squeeze() progress(1, 'Synthesized!') return (24000, gen_wav.numpy()) with gr.Blocks() as app_tts: gr.Markdown("# Galgame Orpheus 3B") ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") gen_text_input = gr.Textbox(label="Text to Generate", lines=10) with gr.Row(): temperature_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.8, step=0.05, label="Temperature") top_p_slider = gr.Slider(minimum=0.0, maximum=1.0, value=1.0, step=0.05, label="Top-p") repetition_penalty_slider = gr.Slider(minimum=1.0, maximum=1.5, value=1.1, step=0.05, label="Repetition Penalty") generate_btn = gr.Button("Synthesize", variant="primary") audio_output = gr.Audio(label="Synthesized Audio") generate_btn.click( infer, inputs=[ ref_audio_input, gen_text_input, temperature_slider, top_p_slider, repetition_penalty_slider, ], outputs=[audio_output], ) with gr.Blocks() as app_credits: gr.Markdown(""" # Credits * [canopyai](https://github.com/canopyai) for the original [repo](https://github.com/canopyai/Orpheus-TTS) * [mrfakename](https://huggingface.co/mrfakename) for the [gradio demo code](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) * [SunderAli17](https://huggingface.co/SunderAli17) for the [gradio demo code](https://huggingface.co/spaces/SunderAli17/llasa-3b-tts) """) with gr.Blocks() as app: gr.Markdown( """ # Galgame Orpheus 3B This is a local web UI for Galgame Orpheus 3B TTS model. You can check out the model [here](https://huggingface.co/NandemoGHS/Galgame-Orpheus-3B). The model is fine-tuned by Japanese audio data. If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. """ ) gr.TabbedInterface([app_tts], ["TTS"]) app.launch()