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AudioGPT / audio_foundation_models.py

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Update audio_foundation_models.py

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	import sys
	import os
	sys.path.append(os.path.dirname(os.path.realpath(__file__)))
	sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
	sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq'))
	sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio'))
	import matplotlib
	import librosa
	from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
	import torch
	from diffusers import StableDiffusionPipeline
	from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
	import re
	import uuid
	import soundfile
	from diffusers import StableDiffusionInpaintPipeline
	from PIL import Image
	import numpy as np
	from omegaconf import OmegaConf
	from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
	import cv2
	import einops
	from einops import repeat
	from pytorch_lightning import seed_everything
	import random
	from ldm.util import instantiate_from_config
	from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000
	from pathlib import Path
	from vocoder.hifigan.modules import VocoderHifigan
	from vocoder.bigvgan.models import VocoderBigVGAN
	from ldm.models.diffusion.ddim import DDIMSampler
	from wav_evaluation.models.CLAPWrapper import CLAPWrapper
	from inference.svs.ds_e2e import DiffSingerE2EInfer
	from audio_to_text.inference_waveform import AudioCapModel
	import whisper
	from text_to_speech.TTS_binding import TTSInference
	from inference.svs.ds_e2e import DiffSingerE2EInfer
	from inference.tts.GenerSpeech import GenerSpeechInfer
	from utils.hparams import set_hparams
	from utils.hparams import hparams as hp
	from utils.os_utils import move_file
	import scipy.io.wavfile as wavfile


	def prompts(name, description):
	def decorator(func):
	func.name = name
	func.description = description
	return func

	return decorator

	def initialize_model(config, ckpt, device):
	config = OmegaConf.load(config)
	model = instantiate_from_config(config.model)
	model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)

	model = model.to(device)
	model.cond_stage_model.to(model.device)
	model.cond_stage_model.device = model.device
	sampler = DDIMSampler(model)
	return sampler

	def initialize_model_inpaint(config, ckpt):
	config = OmegaConf.load(config)
	model = instantiate_from_config(config.model)
	model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)
	device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
	model = model.to(device)
	print(model.device,device,model.cond_stage_model.device)
	sampler = DDIMSampler(model)
	return sampler
	def select_best_audio(prompt,wav_list):
	clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth','text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml',use_cuda=torch.cuda.is_available())
	text_embeddings = clap_model.get_text_embeddings([prompt])
	score_list = []
	for data in wav_list:
	sr,wav = data
	audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav),sr)], resample=True)
	score = clap_model.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy()
	score_list.append(score)
	max_index = np.array(score_list).argmax()
	print(score_list,max_index)
	return wav_list[max_index]


	class T2I:
	def __init__(self, device):
	print("Initializing T2I to %s" % device)
	self.device = device
	self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
	self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
	self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
	self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device)
	self.pipe.to(device)

	@prompts(name="Generate Image From User Input Text",
	description="useful when you want to generate an image from a user input text and save it to a file. "
	"like: generate an image of an object or something, or generate an image that includes some objects. "
	"The input to this tool should be a string, representing the text used to generate image. ")

	def inference(self, text):
	image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
	refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"]
	print(f'{text} refined to {refined_text}')
	image = self.pipe(refined_text).images[0]
	image.save(image_filename)
	print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}")
	return image_filename

	class ImageCaptioning:
	def __init__(self, device):
	print("Initializing ImageCaptioning to %s" % device)
	self.device = device
	self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
	self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device)


	@prompts(name="Remove Something From The Photo",
	description="useful when you want to remove and object or something from the photo "
	"from its description or location. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the object need to be removed. ")

	def inference(self, image_path):
	inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device)
	out = self.model.generate(**inputs)
	captions = self.processor.decode(out[0], skip_special_tokens=True)
	return captions

	class T2A:
	def __init__(self, device):
	print("Initializing Make-An-Audio to %s" % device)
	self.device = device
	self.sampler = initialize_model('text_to_audio/Make_An_Audio/configs/text-to-audio/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta40multi_epoch=000085.ckpt', device=device)
	self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)


	def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
	SAMPLE_RATE = 16000
	prng = np.random.RandomState(seed)
	start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
	start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
	uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
	c = self.sampler.model.get_learned_conditioning(n_samples * [text])
	shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x)
	samples_ddim, _ = self.sampler.sample(S = ddim_steps,
	conditioning = c,
	batch_size = n_samples,
	shape = shape,
	verbose = False,
	unconditional_guidance_scale = scale,
	unconditional_conditioning = uc,
	x_T = start_code)

	x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
	x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1]

	wav_list = []
	for idx,spec in enumerate(x_samples_ddim):
	wav = self.vocoder.vocode(spec)
	wav_list.append((SAMPLE_RATE,wav))
	best_wav = select_best_audio(text, wav_list)
	return best_wav

	@prompts(name="Generate Audio From User Input Text",
	description="useful for when you want to generate an audio "
	"from a user input text and it saved it to a file."
	"The input to this tool should be a string, "
	"representing the text used to generate audio.")

	def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
	melbins,mel_len = 80,624
	with torch.no_grad():
	result = self.txt2audio(
	text = text,
	H = melbins,
	W = mel_len
	)
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	soundfile.write(audio_filename, result[1], samplerate = 16000)
	print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}")
	return audio_filename

	class I2A:
	def __init__(self, device):
	print("Initializing Make-An-Audio-Image to %s" % device)
	self.device = device
	self.sampler = initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt', device=device)
	self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)


	def img2audio(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80):
	SAMPLE_RATE = 16000
	n_samples = 1 # only support 1 sample
	prng = np.random.RandomState(seed)
	start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
	start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
	uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
	#image = Image.fromarray(image)
	image = Image.open(image)
	image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0)
	image_embedding = self.sampler.model.cond_stage_model.forward_img(image)
	c = image_embedding.repeat(n_samples, 1, 1)# shape:[1,77,1280],即还没有变成句子embedding，仍是每个单词的embedding
	shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8] # (z_dim, 80//2^x, 848//2^x)
	samples_ddim, _ = self.sampler.sample(S=ddim_steps,
	conditioning=c,
	batch_size=n_samples,
	shape=shape,
	verbose=False,
	unconditional_guidance_scale=scale,
	unconditional_conditioning=uc,
	x_T=start_code)

	x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
	x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1]
	wav_list = []
	for idx,spec in enumerate(x_samples_ddim):
	wav = self.vocoder.vocode(spec)
	wav_list.append((SAMPLE_RATE,wav))
	best_wav = wav_list[0]
	return best_wav

	@prompts(name="Generate Audio From The Image",
	description="useful for when you want to generate an audio "
	"based on an image. "
	"The input to this tool should be a string, "
	"representing the image_path. ")

	def inference(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80):
	melbins,mel_len = 80,624
	with torch.no_grad():
	result = self.img2audio(
	image=image,
	H=melbins,
	W=mel_len
	)
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	soundfile.write(audio_filename, result[1], samplerate = 16000)
	print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}")
	return audio_filename

	class TTS:
	def __init__(self, device=None):
	self.model = TTSInference(device)

	@prompts(name="Synthesize Speech Given the User Input Text",
	description="useful for when you want to convert a user input text into speech audio it saved it to a file."
	"The input to this tool should be a string, "
	"representing the text used to be converted to speech.")

	def inference(self, text):
	inp = {"text": text}
	out = self.model.infer_once(inp)
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	soundfile.write(audio_filename, out, samplerate = 22050)
	return audio_filename

	class T2S:
	def __init__(self, device= None):
	if device is None:
	device = 'cuda' if torch.cuda.is_available() else 'cpu'
	print("Initializing DiffSinger to %s" % device)
	self.device = device
	self.exp_name = 'checkpoints/0831_opencpop_ds1000'
	self.config= 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml'
	self.set_model_hparams()
	self.pipe = DiffSingerE2EInfer(self.hp, device)
	self.default_inp = {
	'text': '你说你不 SP 懂为何在这时牵手 AP',
	'notes': 'D#4/Eb4 \| D#4/Eb4 \| D#4/Eb4 \| D#4/Eb4 \| rest \| D#4/Eb4 \| D4 \| D4 \| D4 \| D#4/Eb4 \| F4 \| D#4/Eb4 \| D4 \| rest',
	'notes_duration': '0.113740 \| 0.329060 \| 0.287950 \| 0.133480 \| 0.150900 \| 0.484730 \| 0.242010 \| 0.180820 \| 0.343570 \| 0.152050 \| 0.266720 \| 0.280310 \| 0.633300 \| 0.444590'
	}


	def set_model_hparams(self):
	set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
	self.hp = hp

	@prompts(name="Generate Singing Voice From User Input Text, Note and Duration Sequence",
	description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) "
	"and save it to a file."
	"If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence. "
	"If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. "
	"Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx."
	"The input to this tool should be a comma seperated string of three, "
	"representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided. ")

	def inference(self, inputs):
	self.set_model_hparams()
	val = inputs.split(",")
	key = ['text', 'notes', 'notes_duration']
	try:
	inp = {k: v for k, v in zip(key, val)}
	wav = self.pipe.infer_once(inp)
	except:
	print('Error occurs. Generate default audio sample.\n')
	inp = self.default_inp
	wav = self.pipe.infer_once(inp)
	wav *= 32767
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
	print(f"Processed T2S.run, audio_filename: {audio_filename}")
	return audio_filename

	class TTS_OOD:
	def __init__(self, device):
	if device is None:
	device = 'cuda' if torch.cuda.is_available() else 'cpu'
	print("Initializing GenerSpeech to %s" % device)
	self.device = device
	self.exp_name = 'checkpoints/GenerSpeech'
	self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml'
	self.set_model_hparams()
	self.pipe = GenerSpeechInfer(self.hp, device)

	def set_model_hparams(self):
	set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
	f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy'
	if os.path.exists(f0_stats_fn):
	hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn)
	hp['f0_mean'] = float(hp['f0_mean'])
	hp['f0_std'] = float(hp['f0_std'])
	hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt'
	self.hp = hp

	@prompts(name="Style Transfer",
	description="useful for when you want to generate speech samples with styles "
	"(e.g., timbre, emotion, and prosody) derived from a reference custom voice. "
	"Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx."
	"The input to this tool should be a comma seperated string of two, "
	"representing reference audio path and input text. " )

	def inference(self, inputs):
	self.set_model_hparams()
	key = ['ref_audio', 'text']
	val = inputs.split(",")
	inp = {k: v for k, v in zip(key, val)}
	wav = self.pipe.infer_once(inp)
	wav *= 32767
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
	print(
	f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}")
	return audio_filename

	class Inpaint:
	def __init__(self, device):
	print("Initializing Make-An-Audio-inpaint to %s" % device)
	self.device = device
	self.sampler = initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt')
	self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)
	self.cmap_transform = matplotlib.cm.viridis

	def make_batch_sd(self, mel, mask, num_samples=1):

	mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32)
	mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32)
	masked_mel = (1 - mask) * mel

	mel = mel * 2 - 1
	mask = mask * 2 - 1
	masked_mel = masked_mel * 2 -1

	batch = {
	"mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
	"mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples),
	"masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
	}
	return batch
	def gen_mel(self, input_audio_path):
	SAMPLE_RATE = 16000
	sr, ori_wav = wavfile.read(input_audio_path)
	print("gen_mel")
	print(sr,ori_wav.shape,ori_wav)
	ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 # order='C'是以C语言格式存储，不用管
	if len(ori_wav.shape)==2:# stereo
	ori_wav = librosa.to_mono(ori_wav.T)# gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
	print(sr,ori_wav.shape,ori_wav)
	ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE)

	mel_len,hop_size = 848,256
	input_len = mel_len * hop_size
	if len(ori_wav) < input_len:
	input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0)
	else:
	input_wav = ori_wav[:input_len]

	mel = TRANSFORMS_16000(input_wav)
	return mel
	def gen_mel_audio(self, input_audio):
	SAMPLE_RATE = 16000
	sr,ori_wav = input_audio
	print("gen_mel_audio")
	print(sr,ori_wav.shape,ori_wav)

	ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 # order='C'是以C语言格式存储，不用管
	if len(ori_wav.shape)==2:# stereo
	ori_wav = librosa.to_mono(ori_wav.T)# gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
	print(sr,ori_wav.shape,ori_wav)
	ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE)

	mel_len,hop_size = 848,256
	input_len = mel_len * hop_size
	if len(ori_wav) < input_len:
	input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0)
	else:
	input_wav = ori_wav[:input_len]
	mel = TRANSFORMS_16000(input_wav)
	return mel
	def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512):
	model = self.sampler.model

	prng = np.random.RandomState(seed)
	start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8)
	start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)

	c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"]))
	cc = torch.nn.functional.interpolate(batch["mask"],
	size=c.shape[-2:])
	c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask

	shape = (c.shape[1]-1,)+c.shape[2:]
	samples_ddim, _ = self.sampler.sample(S=ddim_steps,
	conditioning=c,
	batch_size=c.shape[0],
	shape=shape,
	verbose=False)
	x_samples_ddim = model.decode_first_stage(samples_ddim)


	mask = batch["mask"]# [-1,1]
	mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0)
	mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0)
	predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0)
	inpainted = (1-mask)mel+maskpredicted_mel
	inpainted = inpainted.cpu().numpy().squeeze()
	inapint_wav = self.vocoder.vocode(inpainted)

	return inpainted, inapint_wav
	def predict(self, input_audio, mel_and_mask, seed = 55, ddim_steps = 100):
	SAMPLE_RATE = 16000
	torch.set_grad_enabled(False)
	mel_img = Image.open(mel_and_mask['image'])
	mask_img = Image.open(mel_and_mask["mask"])
	show_mel = np.array(mel_img.convert("L"))/255 # 由于展示的mel只展示了一部分，所以需要重新从音频生成mel
	mask = np.array(mask_img.convert("L"))/255
	mel_bins,mel_len = 80,848
	input_mel = self.gen_mel_audio(input_audio)[:,:mel_len]# 由于展示的mel只展示了一部分，所以需要重新从音频生成mel
	mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0)# 将mask填充到原来的mel的大小
	print(mask.shape,input_mel.shape)
	with torch.no_grad():
	batch = self.make_batch_sd(input_mel,mask,num_samples=1)
	inpainted,gen_wav = self.inpaint(
	batch=batch,
	seed=seed,
	ddim_steps=ddim_steps,
	num_samples=1,
	H=mel_bins, W=mel_len
	)
	inpainted = inpainted[:,:show_mel.shape[1]]
	color_mel = self.cmap_transform(inpainted)
	input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0])
	gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len]
	image = Image.fromarray((color_mel*255).astype(np.uint8))
	image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
	image.save(image_filename)
	audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
	soundfile.write(audio_filename, gen_wav, samplerate = 16000)
	return image_filename, audio_filename

	@prompts(name="Audio Inpainting",
	description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, "
	"this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input. "
	"The input to this tool should be a string, "
	"representing the audio_path. " )

	def inference(self, input_audio_path):
	crop_len = 500 # the full mel cannot be showed due to gradio's Image bug when using tool='sketch'
	crop_mel = self.gen_mel(input_audio_path)[:,:crop_len]
	color_mel = self.cmap_transform(crop_mel)
	image = Image.fromarray((color_mel*255).astype(np.uint8))
	image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
	image.save(image_filename)
	return image_filename

	class ASR:
	def __init__(self, device):
	print("Initializing Whisper to %s" % device)
	self.device = device
	self.model = whisper.load_model("base", device=device)

	@prompts(name="Transcribe speech",
	description="useful for when you want to know the text corresponding to a human speech, "
	"receives audio_path as input. "
	"The input to this tool should be a string, "
	"representing the audio_path. " )

	def inference(self, audio_path):
	audio = whisper.load_audio(audio_path)
	audio = whisper.pad_or_trim(audio)
	mel = whisper.log_mel_spectrogram(audio).to(self.device)
	_, probs = self.model.detect_language(mel)
	options = whisper.DecodingOptions()
	result = whisper.decode(self.model, mel, options)
	return result.text

	class A2T:
	def __init__(self, device):
	print("Initializing Audio-To-Text Model to %s" % device)
	self.device = device
	self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm")

	@prompts(name="Generate Text From The Audio",
	description="useful for when you want to describe an audio in text, "
	"receives audio_path as input. "
	"The input to this tool should be a string, "
	"representing the audio_path. " )

	def inference(self, audio_path):
	audio = whisper.load_audio(audio_path)
	caption_text = self.model(audio)
	return caption_text[0]