wehub-resource-sync
140 lines
3.8 KiB
Python
140 lines
3.8 KiB
Python
# coding=utf-8
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import librosa
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import base64
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import io
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import gradio as gr
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import re
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import numpy as np
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import torch
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import torchaudio
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# from modelscope import HubApi
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#
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# api = HubApi()
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#
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# api.login('')
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from funasr import AutoModel
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# model = "/Users/zhifu/Downloads/modelscope_models/SenseVoiceCTC"
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# model = "iic/SenseVoiceCTC"
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# model = AutoModel(model=model,
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# vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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# vad_kwargs={"max_single_segment_time": 30000},
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# trust_remote_code=True,
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# )
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import re
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import os
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import sys
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if len(sys.argv) > 1:
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ckpt_dir = sys.argv[1]
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ckpt_id = sys.argv[2]
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jsonl = sys.argv[3]
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output_dir = sys.argv[4]
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device = sys.argv[5]
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new_sys = False
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if len(sys.argv) > 6:
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new_sys = True
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else:
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ckpt_dir = "/nfs/beinian.lzr/workspace/GPT-4o/Exp/exp7/5m-8gpu/exp5-1-0619"
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ckpt_id = "model.pt.ep6"
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jsonl = (
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"/nfs/beinian.lzr/workspace/GPT-4o/Data/Speech2Text/TestData/s2tchat.v20240619.test.jsonl"
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)
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dataset = jsonl.split("/")[-1]
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output_dir = os.path.join(ckpt_dir, f"inference-{ckpt_id}", dataset)
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model = AutoModel(
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model=ckpt_dir,
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init_param=f"{os.path.join(ckpt_dir, ckpt_id)}",
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output_dir=output_dir,
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device=device,
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fp16=False,
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bf16=False,
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llm_dtype="bf16",
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)
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def model_inference(input_wav, text_inputs, fs=16000):
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if isinstance(input_wav, tuple):
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fs, input_wav = input_wav
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input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
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if len(input_wav.shape) > 1:
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input_wav = input_wav.mean(-1)
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if fs != 16000:
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print(f"audio_fs: {fs}")
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resampler = torchaudio.transforms.Resample(fs, 16000)
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input_wav_t = torch.from_numpy(input_wav).to(torch.float32)
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input_wav = resampler(input_wav_t[None, :])[0, :].numpy().astype("float32")
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input_wav_byte = input_wav.tobytes()
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contents_i = []
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system_prompt = text_inputs
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user_prompt = f"<|startofspeech|>!!{input_wav_byte}<|endofspeech|>"
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contents_i.append({"role": "system", "content": system_prompt})
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contents_i.append({"role": "user", "content": user_prompt})
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contents_i.append({"role": "assistant", "content": "target_out"})
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res = model.generate(
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input=[contents_i],
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tearchforing=tearchforing,
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cache={},
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key=key,
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)
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print(res)
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return res
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audio_examples = [
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[
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav",
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"You are a helpful assistant.",
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],
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]
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description = """
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Upload an audio file or input through a microphone, then type te System Prompt.
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"""
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def launch():
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with gr.Blocks() as demo:
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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audio_inputs = gr.Audio(label="Upload audio or use the microphone")
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text_inputs = gr.Text(label="System Prompt", value="You are a helpful assistant.")
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# with gr.Accordion("Configuration"):
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# # task_inputs = gr.Radio(choices=["Speech Recognition", "Rich Text Transcription"],
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# # value="Speech Recognition", label="Task")
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# language_inputs = gr.Dropdown(choices=["auto", "zh", "en", "yue", "ja", "ko", "nospeech"],
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# value="auto",
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# label="Language")
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gr.Examples(examples=audio_examples, inputs=[audio_inputs, text_inputs])
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fn_button = gr.Button("Start")
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text_outputs = gr.HTML(label="Results")
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fn_button.click(model_inference, inputs=[audio_inputs, text_inputs], outputs=text_outputs)
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# with gr.Accordion("More examples"):
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# gr.HTML(centered_table_html)
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demo.launch()
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if __name__ == "__main__":
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# iface.launch()
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launch()
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