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Zero
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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import os
from utils import compute_token_num, load_audio, log_mel_spectrogram, padding_mels
hf_token = os.getenv("HF_TOKEN", None)
os.environ["HF_TOKEN"] = hf_token
class StepAudio2Base:
def __init__(self, model_path: str):
print(model_path)
self.llm_tokenizer = AutoTokenizer.from_pretrained("stepfun-ai/Step-Audio-2-mini", trust_remote_code=True, padding_side="right")
self.llm = AutoModelForCausalLM.from_pretrained("stepfun-ai/Step-Audio-2-mini", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
self.eos_token_id = self.llm_tokenizer.eos_token_id
def __call__(self, messages: list, **kwargs):
messages, mels = self.apply_chat_template(messages)
# Tokenize prompts
prompt_ids = []
for msg in messages:
if isinstance(msg, str):
prompt_ids.append(self.llm_tokenizer(text=msg, return_tensors="pt", padding=True)["input_ids"])
elif isinstance(msg, list):
prompt_ids.append(torch.tensor([msg], dtype=torch.int32))
else:
raise ValueError(f"Unsupported content type: {type(msg)}")
prompt_ids = torch.cat(prompt_ids, dim=-1).cuda()
attention_mask = torch.ones_like(prompt_ids)
#mels = None if len(mels) == 0 else torch.stack(mels).cuda()
#mel_lengths = None if mels is None else torch.tensor([mel.shape[1] - 2 for mel in mels], dtype=torch.int32, device='cuda')
if len(mels)==0:
mels = None
mel_lengths = None
else:
mels, mel_lengths = padding_mels(mels)
mels = mels.cuda()
mel_lengths = mel_lengths.cuda()
generate_inputs = {
"input_ids": prompt_ids,
"wavs": mels,
"wav_lens": mel_lengths,
"attention_mask":attention_mask
}
generation_config = dict(max_new_tokens=2048,
pad_token_id=self.llm_tokenizer.pad_token_id,
eos_token_id=self.eos_token_id,
)
generation_config.update(kwargs)
generation_config = GenerationConfig(**generation_config)
outputs = self.llm.generate(**generate_inputs, generation_config=generation_config)
output_token_ids = outputs[0, prompt_ids.shape[-1] : -1].tolist()
output_text_tokens = [i for i in output_token_ids if i < 151688]
output_audio_tokens = [i - 151696 for i in output_token_ids if i > 151695]
output_text = self.llm_tokenizer.decode(output_text_tokens)
return output_token_ids, output_text, output_audio_tokens
def apply_chat_template(self, messages: list):
results = []
mels = []
for msg in messages:
content = msg
if isinstance(content, str):
text_with_audio = content
results.append(text_with_audio)
elif isinstance(content, dict):
if content["type"] == "text":
results.append(f"{content['text']}")
elif content["type"] == "audio":
audio = load_audio(content['audio'])
for i in range(0, audio.shape[0], 16000 * 25):
mel = log_mel_spectrogram(audio[i:i+16000*25], n_mels=128, padding=479)
mels.append(mel)
audio_tokens = "<audio_patch>" * compute_token_num(mel.shape[1])
results.append(f"<audio_start>{audio_tokens}<audio_end>")
elif content["type"] == "token":
results.append(content["token"])
else:
raise ValueError(f"Unsupported content type: {type(content)}")
# print(results)
return results, mels
class StepAudio2(StepAudio2Base):
def __init__(self, model_path: str):
super().__init__(model_path)
self.llm_tokenizer.eos_token = "<|EOT|>"
self.llm.config.eos_token_id = self.llm_tokenizer.convert_tokens_to_ids("<|EOT|>")
self.eos_token_id = self.llm_tokenizer.convert_tokens_to_ids("<|EOT|>")
def apply_chat_template(self, messages: list):
results = []
mels = []
for msg in messages:
role = msg["role"]
content = msg["content"]
if role == "user":
role = "human"
if isinstance(content, str):
text_with_audio = f"<|BOT|>{role}\n{content}"
text_with_audio += '<|EOT|>' if msg.get('eot', True) else ''
results.append(text_with_audio)
elif isinstance(content, list):
results.append(f"<|BOT|>{role}\n")
for item in content:
if item["type"] == "text":
results.append(f"{item['text']}")
elif item["type"] == "audio":
audio = load_audio(item['audio'])
for i in range(0, audio.shape[0], 16000 * 25):
mel = log_mel_spectrogram(audio[i:i+16000*25], n_mels=128, padding=479)
mels.append(mel)
audio_tokens = "<audio_patch>" * compute_token_num(mel.shape[1])
results.append(f"<audio_start>{audio_tokens}<audio_end>")
elif item["type"] == "token":
results.append(item["token"])
if msg.get('eot', True):
results.append('<|EOT|>')
elif content is None:
results.append(f"<|BOT|>{role}\n")
else:
raise ValueError(f"Unsupported content type: {type(content)}")
# print(results)
return results, mels
if __name__ == '__main__':
from token2wav import Token2wav
model = StepAudio2('stepfun-ai/Step-Audio-2-mini')
token2wav = Token2wav('token2wav')
# Text-to-text conversation
print()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "human", "content": "Give me a brief introduction to the Great Wall."},
{"role": "assistant", "content": None}
]
tokens, text, _ = model(messages, max_new_tokens=256, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
# Text-to-speech conversation
print()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "human", "content": "Give me a brief introduction to the Great Wall."},
{"role": "assistant", "content": "<tts_start>", "eot": False}, # Insert <tts_start> for speech response
]
tokens, text, audio = model(messages, max_new_tokens=4096, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
print(tokens)
audio = token2wav(audio, prompt_wav='assets/default_male.wav')
with open('output-male.wav', 'wb') as f:
f.write(audio)
# Speech-to-text conversation
print()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "human", "content": [{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]},
{"role": "assistant", "content": None}
]
tokens, text, _ = model(messages, max_new_tokens=256, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
# Speech-to-speech conversation
print()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "human", "content": [{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]},
{"role": "assistant", "content": "<tts_start>", "eot": False}, # Insert <tts_start> for speech response
]
tokens, text, audio = model(messages, max_new_tokens=4096, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
print(tokens)
audio = token2wav(audio, prompt_wav='assets/default_female.wav')
with open('output-female.wav', 'wb') as f:
f.write(audio)
# Multi-turn conversation
print()
messages.pop(-1)
messages += [
{"role": "assistant", "content": [{"type": "text", "text": "<tts_start>"},
{"type": "token", "token": tokens}]},
{"role": "human", "content": "Now write a 4-line poem about it."},
{"role": "assistant", "content": None}
]
tokens, text, audio = model(messages, max_new_tokens=256, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
# Multi-modal inputs
print()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "human", "content": [{"type": "text", "text": "Translate the speech into Chinese."},
{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]},
{"role": "assistant", "content": None}
]
tokens, text, audio = model(messages, max_new_tokens=256, temperature=0.7, repetition_penalty=1.05, top_p=0.9, do_sample=True)
print(text)
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