import gradio as gr import os, gc, torch from datetime import datetime from huggingface_hub import hf_hub_download from pynvml import * nvmlInit() gpu_h = nvmlDeviceGetHandleByIndex(0) ctx_limit = 1024 import whisper model1 = whisper.load_model("small") title1 = "RWKV-4-Raven-7B-v8-Eng-20230408-ctx4096" os.environ["RWKV_JIT_ON"] = '1' os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster) from TTS.api import TTS tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True) from rwkv.model import RWKV model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title1}.pth") model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16') from rwkv.utils import PIPELINE, PIPELINE_ARGS pipeline = PIPELINE(model, "20B_tokenizer.json") def generate_prompt(instruction, input=None): if input: return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. # Instruction: {instruction} # Input: {input} # Response: """ else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. # Instruction: {instruction} # Response: """ def evaluate( # instruction, audio, upload, input=None, token_count=200, temperature=1.0, top_p=0.7, presencePenalty = 0.1, countPenalty = 0.1, ): res = [] # load audio and pad/trim it to fit 30 seconds audio = whisper.load_audio(audio) audio = whisper.pad_or_trim(audio) # make log-Mel spectrogram and move to the same device as the model1 mel = whisper.log_mel_spectrogram(audio).to(model1.device) # detect the spoken language _, probs = model1.detect_language(mel) print(f"Detected language: {max(probs, key=probs.get)}") # decode the audio options = whisper.DecodingOptions() result = whisper.decode(model1, mel, options) args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p), alpha_frequency = countPenalty, alpha_presence = presencePenalty, token_ban = [], # ban the generation of some tokens token_stop = [0]) # stop generation whenever you see any token here instruction = result.text.strip() input = input.strip() ctx = generate_prompt(instruction, input) gpu_info = nvmlDeviceGetMemoryInfo(gpu_h) print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') all_tokens = [] out_last = 0 out_str = '' occurrence = {} state = None for i in range(int(token_count)): out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state) for n in occurrence: out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency) token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p) if token in args.token_stop: break all_tokens += [token] if token not in occurrence: occurrence[token] = 1 else: occurrence[token] += 1 tmp = pipeline.decode(all_tokens[out_last:]) if '\ufffd' not in tmp: out_str += tmp yield out_str.strip() out_last = i + 1 gc.collect() torch.cuda.empty_cache() res.append(out_str.strip()) # res1 = ' '.join(str(x) for x in res) tts.tts_to_file(res1, speaker_wav = upload, language="en", file_path="output.wav") # return out_str.strip() # return [result.text, res] return "output.wav" # yield out_str.strip() g = gr.Interface( fn=evaluate, inputs=[ # gr.components.Textbox(lines=2, label="Instruction", value="Tell me about ravens."), gr.Audio(source="microphone", label = "请开始对话吧!", type="filepath"), # gr.Audio(source="upload", label = "请上传您喜欢的声音(wav文件)", type="filepath"), gr.components.Textbox(lines=2, label="Input", placeholder="none"), gr.components.Slider(minimum=10, maximum=200, step=10, value=150), # token_count gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0), # temperature gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.5), # top_p gr.components.Slider(0.0, 1.0, step=0.1, value=0.4), # presencePenalty gr.components.Slider(0.0, 1.0, step=0.1, value=0.4), # countPenalty ], outputs=[ # gr.inputs.Textbox( # lines=5, # label="Raven Output", # ), gr.Audio(label="Audio with Custom Voice"), ], title="🥳💬💕 - TalktoAI,随时随地,谈天说地!", description="🤖 - 让有人文关怀的AI造福每一个人!AI向善,文明璀璨!TalktoAI - Enable the future!", article = "Powered by the RWKV Language Model" ) g.queue(concurrency_count=1, max_size=10) g.launch(show_error=True)