233 lines
8.7 KiB
Python
233 lines
8.7 KiB
Python
import io
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import re
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import wave
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import gradio as gr
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import numpy as np
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from .fish_e2e import FishE2EAgent, FishE2EEventType
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from .schema import ServeMessage, ServeTextPart, ServeVQPart
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def wav_chunk_header(sample_rate=44100, bit_depth=16, channels=1):
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buffer = io.BytesIO()
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with wave.open(buffer, "wb") as wav_file:
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wav_file.setnchannels(channels)
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wav_file.setsampwidth(bit_depth // 8)
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wav_file.setframerate(sample_rate)
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wav_header_bytes = buffer.getvalue()
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buffer.close()
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return wav_header_bytes
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class ChatState:
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def __init__(self):
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self.conversation = []
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self.added_systext = False
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self.added_sysaudio = False
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def get_history(self):
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results = []
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for msg in self.conversation:
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results.append({"role": msg.role, "content": self.repr_message(msg)})
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# Process assistant messages to extract questions and update user messages
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for i, msg in enumerate(results):
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if msg["role"] == "assistant":
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match = re.search(r"Question: (.*?)\n\nResponse:", msg["content"])
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if match and i > 0 and results[i - 1]["role"] == "user":
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# Update previous user message with extracted question
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results[i - 1]["content"] += "\n" + match.group(1)
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# Remove the Question/Answer format from assistant message
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msg["content"] = msg["content"].split("\n\nResponse: ", 1)[1]
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return results
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def repr_message(self, msg: ServeMessage):
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response = ""
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for part in msg.parts:
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if isinstance(part, ServeTextPart):
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response += part.text
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elif isinstance(part, ServeVQPart):
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response += f"<audio {len(part.codes[0]) / 21:.2f}s>"
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return response
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def clear_fn():
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return [], ChatState(), None, None, None
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async def process_audio_input(
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sys_audio_input, sys_text_input, audio_input, state: ChatState, text_input: str
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):
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if audio_input is None and not text_input:
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raise gr.Error("No input provided")
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agent = FishE2EAgent() # Create new agent instance for each request
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# Convert audio input to numpy array
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if isinstance(audio_input, tuple):
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sr, audio_data = audio_input
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elif text_input:
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sr = 44100
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audio_data = None
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else:
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raise gr.Error("Invalid audio format")
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if isinstance(sys_audio_input, tuple):
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sr, sys_audio_data = sys_audio_input
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else:
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sr = 44100
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sys_audio_data = None
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def append_to_chat_ctx(
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part: ServeTextPart | ServeVQPart, role: str = "assistant"
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) -> None:
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if not state.conversation or state.conversation[-1].role != role:
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state.conversation.append(ServeMessage(role=role, parts=[part]))
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else:
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state.conversation[-1].parts.append(part)
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if state.added_systext is False and sys_text_input:
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state.added_systext = True
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append_to_chat_ctx(ServeTextPart(text=sys_text_input), role="system")
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if text_input:
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append_to_chat_ctx(ServeTextPart(text=text_input), role="user")
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audio_data = None
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result_audio = b""
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async for event in agent.stream(
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sys_audio_data,
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audio_data,
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sr,
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1,
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chat_ctx={
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"messages": state.conversation,
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"added_sysaudio": state.added_sysaudio,
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},
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):
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if event.type == FishE2EEventType.USER_CODES:
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append_to_chat_ctx(ServeVQPart(codes=event.vq_codes), role="user")
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elif event.type == FishE2EEventType.SPEECH_SEGMENT:
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append_to_chat_ctx(ServeVQPart(codes=event.vq_codes))
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yield state.get_history(), wav_chunk_header() + event.frame.data, None, None
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elif event.type == FishE2EEventType.TEXT_SEGMENT:
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append_to_chat_ctx(ServeTextPart(text=event.text))
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yield state.get_history(), None, None, None
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yield state.get_history(), None, None, None
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async def process_text_input(
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sys_audio_input, sys_text_input, state: ChatState, text_input: str
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):
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async for event in process_audio_input(
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sys_audio_input, sys_text_input, None, state, text_input
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):
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yield event
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def create_demo():
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with gr.Blocks() as demo:
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state = gr.State(ChatState())
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with gr.Row():
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# Left column (70%) for chatbot and notes
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with gr.Column(scale=7):
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chatbot = gr.Chatbot(
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[],
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elem_id="chatbot",
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bubble_full_width=False,
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height=600,
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type="messages",
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)
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# notes = gr.Markdown(
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# """
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# # Fish Agent
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# 1. 此Demo为Fish Audio自研端到端语言模型Fish Agent 3B版本.
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# 2. 你可以在我们的官方仓库找到代码以及权重，但是相关内容全部基于 CC BY-NC-SA 4.0 许可证发布.
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# 3. Demo为早期灰度测试版本，推理速度尚待优化.
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# # 特色
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# 1. 该模型自动集成ASR与TTS部分，不需要外挂其它模型，即真正的端到端，而非三段式(ASR+LLM+TTS).
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# 2. 模型可以使用reference audio控制说话音色.
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# 3. 可以生成具有较强情感与韵律的音频.
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# """
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# )
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notes = gr.Markdown(
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"""
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# Fish Agent
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1. This demo is Fish Audio's self-researh end-to-end language model, Fish Agent version 3B.
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2. You can find the code and weights in our official repo in [gitub](https://github.com/fishaudio/fish-speech) and [hugging face](https://huggingface.co/fishaudio/fish-agent-v0.1-3b), but the content is released under a CC BY-NC-SA 4.0 licence.
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3. The demo is an early alpha test version, the inference speed needs to be optimised.
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# Features
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1. The model automatically integrates ASR and TTS parts, no need to plug-in other models, i.e., true end-to-end, not three-stage (ASR+LLM+TTS).
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2. The model can use reference audio to control the speech timbre.
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3. The model can generate speech with strong emotion.
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"""
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)
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# Right column (30%) for controls
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with gr.Column(scale=3):
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sys_audio_input = gr.Audio(
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sources=["upload"],
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type="numpy",
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label="Give a timbre for your assistant",
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)
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sys_text_input = gr.Textbox(
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label="What is your assistant's role?",
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value="You are a voice assistant created by Fish Audio, offering end-to-end voice interaction for a seamless user experience. You are required to first transcribe the user's speech, then answer it in the following format: 'Question: [USER_SPEECH]\n\nAnswer: [YOUR_RESPONSE]\n'. You are required to use the following voice in this conversation.",
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type="text",
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)
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audio_input = gr.Audio(
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sources=["microphone"], type="numpy", label="Speak your message"
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)
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text_input = gr.Textbox(label="Or type your message", type="text")
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output_audio = gr.Audio(
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label="Assistant's Voice",
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streaming=True,
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autoplay=True,
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interactive=False,
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)
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send_button = gr.Button("Send", variant="primary")
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clear_button = gr.Button("Clear")
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# Event handlers
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audio_input.stop_recording(
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process_audio_input,
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inputs=[sys_audio_input, sys_text_input, audio_input, state, text_input],
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outputs=[chatbot, output_audio, audio_input, text_input],
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show_progress=True,
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)
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send_button.click(
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process_text_input,
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inputs=[sys_audio_input, sys_text_input, state, text_input],
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outputs=[chatbot, output_audio, audio_input, text_input],
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show_progress=True,
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)
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text_input.submit(
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process_text_input,
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inputs=[sys_audio_input, sys_text_input, state, text_input],
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outputs=[chatbot, output_audio, audio_input, text_input],
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show_progress=True,
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)
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clear_button.click(
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clear_fn,
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inputs=[],
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outputs=[chatbot, state, audio_input, output_audio, text_input],
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)
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return demo
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if __name__ == "__main__":
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demo = create_demo()
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demo.launch(server_name="127.0.0.1", server_port=7860, share=True)
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