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import json
import os.path
import tempfile
import sys
import re
import uuid
import requests
from argparse import ArgumentParser
import torchaudio
from transformers import WhisperFeatureExtractor, AutoTokenizer
from speech_tokenizer.modeling_whisper import WhisperVQEncoder
sys.path.insert(0, "./cosyvoice")
sys.path.insert(0, "./third_party/Matcha-TTS")
from speech_tokenizer.utils import extract_speech_token
import gradio as gr
import torch
audio_token_pattern = re.compile(r"<\|audio_(\d+)\|>")
from flow_inference import AudioDecoder
from audio_process import AudioStreamProcessor
use_local_interface = True
if use_local_interface :
from model_server import ModelWorker
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--host", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int, default="8888")
parser.add_argument("--flow-path", type=str, default="./glm-4-voice-decoder")
parser.add_argument("--model-path", type=str, default="THUDM/glm-4-voice-9b")
parser.add_argument("--tokenizer-path", type= str, default="THUDM/glm-4-voice-tokenizer")
args = parser.parse_args()
flow_config = os.path.join(args.flow_path, "config.yaml")
flow_checkpoint = os.path.join(args.flow_path, 'flow.pt')
hift_checkpoint = os.path.join(args.flow_path, 'hift.pt')
glm_tokenizer = None
device = "cpu"
audio_decoder: AudioDecoder = None
whisper_model, feature_extractor = None, None
def initialize_fn():
global audio_decoder, feature_extractor, whisper_model, glm_model, glm_tokenizer
if audio_decoder is not None:
return
# GLM
glm_tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
# Flow & Hift
audio_decoder = AudioDecoder(config_path=flow_config, flow_ckpt_path=flow_checkpoint,
hift_ckpt_path=hift_checkpoint,
device=device)
# Speech tokenizer
whisper_model = WhisperVQEncoder.from_pretrained(args.tokenizer_path).eval().to(device)
feature_extractor = WhisperFeatureExtractor.from_pretrained(args.tokenizer_path)
global use_local_interface, worker
if use_local_interface :
model_path0 = 'THUDM/glm-4-voice-9b'
# dtype = 'bfloat16'
#device = 'cpu'
worker = ModelWorker(model_path0,device)
def clear_fn():
return [], [], '', '', '', None, None
def inference_fn(
temperature: float,
top_p: float,
max_new_token: int,
input_mode,
audio_path: str | None,
input_text: str | None,
history: list[dict],
previous_input_tokens: str,
previous_completion_tokens: str,
):
if input_mode == "audio":
assert audio_path is not None
history.append({"role": "user", "content": {"path": audio_path}})
audio_tokens = extract_speech_token(
whisper_model, feature_extractor, [audio_path]
)[0]
if len(audio_tokens) == 0:
raise gr.Error("No audio tokens extracted")
audio_tokens = "".join([f"<|audio_{x}|>" for x in audio_tokens])
audio_tokens = "<|begin_of_audio|>" + audio_tokens + "<|end_of_audio|>"
user_input = audio_tokens
system_prompt = "User will provide you with a speech instruction. Do it step by step. First, think about the instruction and respond in a interleaved manner, with 13 text token followed by 26 audio tokens. "
else:
assert input_text is not None
history.append({"role": "user", "content": input_text})
user_input = input_text
system_prompt = "User will provide you with a text instruction. Do it step by step. First, think about the instruction and respond in a interleaved manner, with 13 text token followed by 26 audio tokens."
# Gather history
inputs = previous_input_tokens + previous_completion_tokens
inputs = inputs.strip()
if "<|system|>" not in inputs:
inputs += f"<|system|>\n{system_prompt}"
inputs += f"<|user|>\n{user_input}<|assistant|>streaming_transcription\n"
global use_local_interface , worker
with torch.no_grad():
if use_local_interface :
params = { "prompt": inputs,
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_token, }
response = worker.generate_stream( params )
else :
response = requests.post(
"http://localhost:10000/generate_stream",
data=json.dumps({
"prompt": inputs,
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_token,
}),
stream=True
)
text_tokens, audio_tokens = [], []
audio_offset = glm_tokenizer.convert_tokens_to_ids('<|audio_0|>')
end_token_id = glm_tokenizer.convert_tokens_to_ids('<|user|>')
complete_tokens = []
prompt_speech_feat = torch.zeros(1, 0, 80).to(device)
flow_prompt_speech_token = torch.zeros(1, 0, dtype=torch.int64).to(device)
this_uuid = str(uuid.uuid4())
tts_speechs = []
tts_mels = []
prev_mel = None
is_finalize = False
block_size_list = [25,50,100,150,200]
block_size_idx = 0
block_size = block_size_list[block_size_idx]
audio_processor = AudioStreamProcessor()
for chunk in response.iter_lines():
token_id = json.loads(chunk)["token_id"]
if token_id == end_token_id:
is_finalize = True
if len(audio_tokens) >= block_size or (is_finalize and audio_tokens):
if block_size_idx < len(block_size_list) - 1:
block_size_idx += 1
block_size = block_size_list[block_size_idx]
tts_token = torch.tensor(audio_tokens, device=device).unsqueeze(0)
if prev_mel is not None:
prompt_speech_feat = torch.cat(tts_mels, dim=-1).transpose(1, 2)
tts_speech, tts_mel = audio_decoder.token2wav(tts_token, uuid=this_uuid,
prompt_token=flow_prompt_speech_token.to(device),
prompt_feat=prompt_speech_feat.to(device),
finalize=is_finalize)
prev_mel = tts_mel
audio_bytes = audio_processor.process(tts_speech.clone().cpu().numpy()[0], last=is_finalize)
tts_speechs.append(tts_speech.squeeze())
tts_mels.append(tts_mel)
if audio_bytes:
yield history, inputs, '', '', audio_bytes, None
flow_prompt_speech_token = torch.cat((flow_prompt_speech_token, tts_token), dim=-1)
audio_tokens = []
if not is_finalize:
complete_tokens.append(token_id)
if token_id >= audio_offset:
audio_tokens.append(token_id - audio_offset)
else:
text_tokens.append(token_id)
tts_speech = torch.cat(tts_speechs, dim=-1).cpu()
complete_text = glm_tokenizer.decode(complete_tokens, spaces_between_special_tokens=False)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
torchaudio.save(f, tts_speech.unsqueeze(0), 22050, format="wav")
history.append({"role": "assistant", "content": {"path": f.name, "type": "audio/wav"}})
history.append({"role": "assistant", "content": glm_tokenizer.decode(text_tokens, ignore_special_tokens=False)})
yield history, inputs, complete_text, '', None, (22050, tts_speech.numpy())
def update_input_interface(input_mode):
if input_mode == "audio":
return [gr.update(visible=True), gr.update(visible=False)]
else:
return [gr.update(visible=False), gr.update(visible=True)]
# Create the Gradio interface
with gr.Blocks(title="GLM-4-Voice Demo", fill_height=True) as demo:
with gr.Row():
temperature = gr.Number(
label="Temperature",
value=0.2
)
top_p = gr.Number(
label="Top p",
value=0.8
)
max_new_token = gr.Number(
label="Max new tokens",
value=2000,
)
chatbot = gr.Chatbot(
elem_id="chatbot",
bubble_full_width=False,
type="messages",
scale=1,
)
with gr.Row():
with gr.Column():
input_mode = gr.Radio(["audio", "text"], label="Input Mode", value="audio")
audio = gr.Audio(label="Input audio", type='filepath', show_download_button=True, visible=True)
text_input = gr.Textbox(label="Input text", placeholder="Enter your text here...", lines=2, visible=False)
with gr.Column():
submit_btn = gr.Button("Submit")
reset_btn = gr.Button("Clear")
output_audio = gr.Audio(label="Play", streaming=True,
autoplay=True, show_download_button=False)
complete_audio = gr.Audio(label="Last Output Audio (If Any)", show_download_button=True)
gr.Markdown("""## Debug Info""")
with gr.Row():
input_tokens = gr.Textbox(
label=f"Input Tokens",
interactive=False,
)
completion_tokens = gr.Textbox(
label=f"Completion Tokens",
interactive=False,
)
detailed_error = gr.Textbox(
label=f"Detailed Error",
interactive=False,
)
history_state = gr.State([])
respond = submit_btn.click(
inference_fn,
inputs=[
temperature,
top_p,
max_new_token,
input_mode,
audio,
text_input,
history_state,
input_tokens,
completion_tokens,
],
outputs=[history_state, input_tokens, completion_tokens, detailed_error, output_audio, complete_audio]
)
respond.then(lambda s: s, [history_state], chatbot)
reset_btn.click(clear_fn, outputs=[chatbot, history_state, input_tokens, completion_tokens, detailed_error, output_audio, complete_audio])
input_mode.input(clear_fn, outputs=[chatbot, history_state, input_tokens, completion_tokens, detailed_error, output_audio, complete_audio]).then(update_input_interface, inputs=[input_mode], outputs=[audio, text_input])
initialize_fn()
# Launch the interface
demo.launch()
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