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| 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) |