Spaces:
Sleeping
Sleeping
Samuel L Meyers
commited on
Commit
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d487976
1
Parent(s):
06ae9a8
AAAAH
Browse files
app.py
CHANGED
@@ -9,11 +9,14 @@ import glob
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import logging
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from typing import cast
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from threading import Lock
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from transformers import pipeline
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import gradio as gr
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from balacoon_tts import TTS
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from huggingface_hub import hf_hub_download, list_repo_files
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# locker that disallow access to the tts object from more then one thread
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locker = Lock()
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@@ -37,12 +40,35 @@ stt_pipe = pipeline(
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model="openai/whisper-large-v3",
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)
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def transcribe_stt(audio):
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if audio is None:
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = stt_pipe(audio, generate_kwargs={"language": "english", "task": "transcribe"})["text"]
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return text
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def main():
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logging.basicConfig(level=logging.INFO)
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@@ -116,6 +142,14 @@ def main():
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with gr.Column(variant="panel"):
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stt_transcribe_output = gr.Textbox()
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stt_transcribe_btn = gr.Button("Transcribe")
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def synthesize_audio(text_str: str, model_name_str: str, speaker_str: str):
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"""
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@@ -144,6 +178,7 @@ def main():
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generate.click(synthesize_audio, inputs=[text, model_name, speaker], outputs=audio, api_name="synthesize")
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stt_transcribe_btn.click(transcribe_stt, inputs=stt_input_file, outputs=stt_transcribe_output, api_name="transcribe")
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demo.queue(concurrency_count=1).launch()
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import logging
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from typing import cast
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from threading import Lock
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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from balacoon_tts import TTS
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from huggingface_hub import hf_hub_download, list_repo_files
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import torch
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from conversation import get_default_conv_template
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# locker that disallow access to the tts object from more then one thread
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locker = Lock()
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model="openai/whisper-large-v3",
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)
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talkers = {
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"m3b": {
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"tokenizer": AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False),
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"model": AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", device_map="auto"),
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"conv": get_default_conv_template("minichat")
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}
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}
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def transcribe_stt(audio):
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if audio is None:
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raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
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text = stt_pipe(audio, generate_kwargs={"language": "english", "task": "transcribe"})["text"]
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return text
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def m3b_talk(text):
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m3bconv = talkers["m3b"]["conv"]
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m3bconv.append_message(m3bconv.roles[0], text)
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m3bconv.append_message(m3bconv.roles[1], None)
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input_ids = talkers["m3b"]["tokenizer"]([text]).input_ids
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response_tokens = talkers["m3b"]["model"](
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torch.as_tensor(m3bconv.get_prompt()),
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do_sample=True,
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temperature=0.2,
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max_new_tokens=1024,
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)
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response_tokens = response_tokens[0][len(input_ids[0]):]
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response = talkers["m3b"]["tokenizer"].decode(response_tokens, skip_special_tokens=True).strip()
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return response
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def main():
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logging.basicConfig(level=logging.INFO)
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with gr.Column(variant="panel"):
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stt_transcribe_output = gr.Textbox()
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stt_transcribe_btn = gr.Button("Transcribe")
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with gr.Row(variant="panel"):
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gr.Markdown("## Talk to MiniChat-3B\n\nTalk to MiniChat-3B.")
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with gr.Row(variant="panel"):
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with gr.Column(variant="panel"):
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m3b_talk_input = gr.Textbox(label="Message", placeholder="Type something here...")
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with gr.Column(variant="panel"):
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m3b_talk_output = gr.Textbox()
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m3b_talk_btn = gr.Button("Send")
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def synthesize_audio(text_str: str, model_name_str: str, speaker_str: str):
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"""
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generate.click(synthesize_audio, inputs=[text, model_name, speaker], outputs=audio, api_name="synthesize")
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stt_transcribe_btn.click(transcribe_stt, inputs=stt_input_file, outputs=stt_transcribe_output, api_name="transcribe")
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m3b_talk_btn.click(m3b_talk, inputs=m3b_talk_input, outputs=m3b_talk_output, api_name="talk_m3b")
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demo.queue(concurrency_count=1).launch()
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