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Zero
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import gradio as gr
import torch
import torchaudio
from encodec import EncodecModel
from whisperspeech.vq_stoks import RQBottleneckTransformer
from encodec.utils import convert_audio
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import logging
import os
from generate_audio import (
TTSProcessor,
)
import uuid
device = "cuda" if torch.cuda.is_available() else "cpu"
vq_model = RQBottleneckTransformer.load_model(
"whisper-vq-stoks-medium-en+pl-fixed.model"
).to(device)
vq_model.ensure_whisper(device)
def audio_to_sound_tokens_whisperspeech(audio_path):
wav, sr = torchaudio.load(audio_path)
if sr != 16000:
wav = torchaudio.functional.resample(wav, sr, 16000)
with torch.no_grad():
codes = vq_model.encode_audio(wav.to(device))
codes = codes[0].cpu().tolist()
result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
return f'<|sound_start|>{result}<|sound_end|>'
def audio_to_sound_tokens_whisperspeech_transcribe(audio_path):
wav, sr = torchaudio.load(audio_path)
if sr != 16000:
wav = torchaudio.functional.resample(wav, sr, 16000)
with torch.no_grad():
codes = vq_model.encode_audio(wav.to(device))
codes = codes[0].cpu().tolist()
result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
return f'<|reserved_special_token_69|><|sound_start|>{result}<|sound_end|>'
def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device="cuda"):
model = EncodecModel.encodec_model_24khz()
model.set_target_bandwidth(target_bandwidth)
model.to(device)
wav, sr = torchaudio.load(audio_path)
wav = convert_audio(wav, sr, model.sample_rate, model.channels)
wav = wav.unsqueeze(0).to(device)
with torch.no_grad():
encoded_frames = model.encode(wav)
codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1)
audio_code1, audio_code2 = codes[0][0], codes[0][1]
flatten_tokens = torch.stack((audio_code1, audio_code2), dim=1).flatten().tolist()
result = ''.join(f'<|sound_{num:04d}|>' for num in flatten_tokens)
return f'<|sound_start|>{result}<|sound_end|>'
def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
tokenizer = AutoTokenizer.from_pretrained(model_path)
model_kwargs = {"device_map": "auto"}
if use_8bit:
model_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_enable_fp32_cpu_offload=False,
llm_int8_has_fp16_weight=False,
)
else:
model_kwargs["torch_dtype"] = torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
return pipeline("text-generation", model=model, tokenizer=tokenizer)
tts = TTSProcessor(device)
llm_path = "homebrewltd/Llama3.1-s-instruct-2024-08-19-epoch-3"
pipe = setup_pipeline(llm_path, use_8bit=False)
tokenizer = pipe.tokenizer
model = pipe.model
# print(tokenizer.encode("<|sound_0001|>", add_special_tokens=False))# return the audio tensor
# print(tokenizer.eos_token)
def text_to_audio_file(text):
# gen a random id for the audio file
id = str(uuid.uuid4())
temp_file = f"./user_audio/{id}_temp_audio.wav"
text = text
text_split = "_".join(text.lower().split(" "))
# remove the last character if it is a period
if text_split[-1] == ".":
text_split = text_split[:-1]
tts.convert_text_to_audio_file(text, temp_file)
# logging.info(f"Saving audio to {temp_file}")
# torchaudio.save(temp_file, audio.cpu(), sample_rate=24000)
print(f"Saved audio to {temp_file}")
return temp_file
def process_input(input_type, text_input=None, audio_file=None):
# if input_type == "text":
# audio_file = "temp_audio.wav"
for partial_message in process_audio(audio_file):
yield partial_message
# if input_type == "text":
# os.remove(audio_file)
def process_transcribe_input(input_type, text_input=None, audio_file=None):
# if input_type == "text":
# audio_file = "temp_audio.wav"
for partial_message in process_audio(audio_file, transcript=True):
yield partial_message
# if input_type == "text":
# os.remove(audio_file)
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
# encode </s> token
stop_ids = [tokenizer.eos_token_id, 128009] # Adjust this based on your model's tokenizer
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def process_audio(audio_file, transcript=False):
if audio_file is None:
raise ValueError("No audio file provided")
logging.info(f"Audio file received: {audio_file}")
logging.info(f"Audio file type: {type(audio_file)}")
sound_tokens = audio_to_sound_tokens_whisperspeech_transcribe(audio_file) if transcript else audio_to_sound_tokens_whisperspeech(audio_file)
logging.info("Sound tokens generated successfully")
# logging.info(f"audio_file: {audio_file.name}")
messages = [
{"role": "user", "content": sound_tokens},
]
stop = StopOnTokens()
input_str = tokenizer.apply_chat_template(messages, tokenize=False)
input_ids = tokenizer.encode(input_str, return_tensors="pt")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=1024,
do_sample=False,
stopping_criteria=StoppingCriteriaList([stop])
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
partial_message = ""
for new_token in streamer:
partial_message += new_token
if tokenizer.eos_token in partial_message:
break
partial_message = partial_message.replace("assistant\n\n", "")
yield partial_message
# def stop_generation():
# # This is a placeholder. Implement actual stopping logic here if needed.
# return "Generation stopped.", gr.Button.update(interactive=False)
# take all the examples from the examples folder
good_examples = []
for file in os.listdir("./examples"):
if file.endswith(".wav"):
good_examples.append([f"./examples/{file}"])
bad_examples = []
for file in os.listdir("./bad_examples"):
if file.endswith(".wav"):
bad_examples.append([f"./bad_examples/{file}"])
examples = []
examples.extend(good_examples)
examples.extend(bad_examples)
# with gr.Blocks() as iface:
# gr.Markdown("# Llama3-S: A Speech & Text Fusion Model Checkpoint from Homebrew")
# gr.Markdown("Enter text or upload a .wav file to generate text based on its content.")
# with gr.Row():
# input_type = gr.Radio(["text", "audio"], label="Input Type", value="audio")
# text_input = gr.Textbox(label="Text Input", visible=False)
# audio_input = gr.Audio(sources=["upload"], type="filepath", label="Upload audio", visible=True)
# output = gr.Textbox(label="Generated Text")
# submit_button = gr.Button("Submit")
# input_type.change(
# update_visibility,
# inputs=[input_type],
# outputs=[text_input, audio_input]
# )
# submit_button.click(
# process_input,
# inputs=[input_type, text_input, audio_input],
# outputs=[output]
# )
# gr.Examples(examples, inputs=[audio_input])
# iface.launch(server_name="127.0.0.1", server_port=8080)
with gr.Blocks() as iface:
gr.Markdown("# Llama3-1-S: checkpoint Aug 19, 2024")
gr.Markdown("Enter text to convert to audio, then submit the audio to generate text or Upload Audio")
with gr.Row():
input_type = gr.Radio(["text", "audio"], label="Input Type", value="audio")
text_input = gr.Textbox(label="Text Input", visible=False)
audio_input = gr.Audio(label="Audio", type="filepath", visible=True)
# audio_output = gr.Audio(label="Converted Audio", type="filepath", visible=False)
convert_button = gr.Button("Convert to Audio", visible=False)
submit_button = gr.Button("Submit for Processing")
transcrip_button = gr.Button("Please Transcribe the audio for me")
text_output = gr.Textbox(label="Generated Text")
def update_visibility(input_type):
return (gr.update(visible=input_type == "text"),
gr.update(visible=input_type == "text"))
def convert_and_display(text):
audio_file = text_to_audio_file(text)
return audio_file
def process_example(file_path):
return update_visibility("audio")
input_type.change(
update_visibility,
inputs=[input_type],
outputs=[text_input, convert_button]
)
convert_button.click(
convert_and_display,
inputs=[text_input],
outputs=[audio_input]
)
submit_button.click(
process_input,
inputs=[input_type, text_input, audio_input],
outputs=[text_output]
)
transcrip_button.click(
process_transcribe_input,
inputs=[input_type, text_input, audio_input],
outputs=[text_output]
)
gr.Examples(examples, inputs=[audio_input],outputs=[audio_input], fn=process_example)
iface.queue()
iface.launch()
# launch locally
# iface.launch(server_name="0.0.0.0")
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