Spaces:
No application file
No application file
File size: 3,852 Bytes
d4f129c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # -*- coding: utf-8 -*-
"""Untitled2.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1dwiOTRmj8MDuVOgv2OKzE7qx3UXPKCQH
"""
!pip install gradio git+https://github.com/huggingface/transformers.git sentencepiece torchaudio
# Install necessary packages
# !pip install gradio git+https://github.com/huggingface/transformers.git sentencepiece torchaudio
import gradio as gr
from transformers import AutoProcessor, SeamlessM4Tv2Model
import torchaudio
import numpy as np
# Load the processor and model
processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
sample_rate = model.config.sampling_rate
# Text-to-Speech function
def text_to_speech(text, src_lang="eng", tgt_lang="arb"):
text_inputs = processor(text=text, src_lang=src_lang, return_tensors="pt")
audio_array_from_text = model.generate(**text_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()
return sample_rate, audio_array_from_text
# Speech-to-Speech function
def speech_to_speech(audio, src_lang="eng", tgt_lang="rus"):
audio, orig_freq = torchaudio.load(audio)
audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000) # Must be a 16 kHz waveform array
audio_inputs = processor(audios=audio, return_tensors="pt")
audio_array_from_audio = model.generate(**audio_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()
return sample_rate, audio_array_from_audio
# Speech-to-Text function
def speech_to_text(audio, src_lang="eng", tgt_lang="ces"):
audio, orig_freq = torchaudio.load(audio)
audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000) # Must be a 16 kHz waveform array
audio_inputs = processor(audios=audio, return_tensors="pt")
output_tokens = model.generate(**audio_inputs, tgt_lang=tgt_lang, generate_speech=False)
translated_text_from_audio = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
return translated_text_from_audio
# Text-to-Text function
def text_to_text(text, src_lang="eng", tgt_lang="ces"):
text_inputs = processor(text=text, src_lang=src_lang, return_tensors="pt")
output_tokens = model.generate(**text_inputs, tgt_lang=tgt_lang, generate_speech=False)
translated_text_from_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
return translated_text_from_text
# Create Gradio interfaces
text_to_speech_interface = gr.Interface(
fn=text_to_speech,
inputs=[gr.Textbox(label="Input Text"), gr.Textbox(label="Source Language", value="eng"), gr.Textbox(label="Target Language", value="arb")],
outputs=[gr.Audio(label="Output Audio")]
)
speech_to_speech_interface = gr.Interface(
fn=speech_to_speech,
inputs=[gr.Audio(type="filepath"), gr.Textbox(label="Source Language", value="eng"), gr.Textbox(label="Target Language", value="rus")],
outputs=[gr.Audio(label="Output Audio")]
)
speech_to_text_interface = gr.Interface(
fn=speech_to_text,
inputs=[gr.Audio(type="filepath"), gr.Textbox(label="Source Language", value="eng"), gr.Textbox(label="Target Language", value="ces")],
outputs=gr.Textbox(label="Translated Text")
)
text_to_text_interface = gr.Interface(
fn=text_to_text,
inputs=[gr.Textbox(label="Input Text"), gr.Textbox(label="Source Language", value="eng"), gr.Textbox(label="Target Language", value="ces")],
outputs=gr.Textbox(label="Translated Text")
)
# Combine all interfaces into a single tabbed interface
app = gr.TabbedInterface(
[text_to_speech_interface, speech_to_speech_interface, speech_to_text_interface, text_to_text_interface],
["Text-to-Speech", "Speech-to-Speech", "Speech-to-Text", "Text-to-Text"]
)
# Launch the app
app.launch()
|