Hugging Face's logo

Spaces:
cotxetj
/
swedish-to-speech-or-text
Runtime error

App Files Community
swedish-to-speech-or-text
File size: 2,486 Bytes 
0668b67
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import torch
from transformers import pipeline, VitsModel, VitsTokenizer
import numpy as np
import gradio as gr

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Load Whisper-small
pipe = pipeline("automatic-speech-recognition",
                model="openai/whisper-small",
                device=device
)

# Load the model checkpoint and tokenizer
#model = VitsModel.from_pretrained("Matthijs/mms-tts-fra")
#tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra")
model = VitsModel.from_pretrained("facebook/mms-tts-fra")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")


# Define a function to translate an audio, in english here
def translate(audio):
    outputs = pipe(audio, max_new_tokens=256,
                   generate_kwargs={"task": "transcribe", "language": "eng"})
    return outputs["text"]


# Define function to generate the waveform output
def synthesise(text):
    inputs = tokenizer(text, return_tensors="pt")
    input_ids = inputs["input_ids"]

    with torch.no_grad(): 
      outputs = model(input_ids)
    
    return outputs.audio[0]


# Define the pipeline
def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesised_speech = synthesise(translated_text)
    synthesised_speech = (
        synthesised_speech.numpy() * 32767).astype(np.int16)
    return 16000, synthesised_speech

def predict(transType, language, audio, audio_mic = None):
        if not audio and audio_mic:
            audio = audio_mic
        if transType == "Text":
            return translate(audio)
        if transType == "Audio":
            return speech_to_speech_translation(audio)
            
# Define the title etc
title = "Swedish STSOT (Speech To Speech Or Text)"
description="Use Whisper pretrained model to convert swedish audio to english (text or audio)"

demo = gr.Blocks()

supportLangs = ["Swedish", "French (in training)"]
transTypes = ["Text", "Audio"]
demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Radio(label="Choose your output format", choices=transTypes),
        gr.Radio(label="Choose a source language", choices=supportLangs, value="Swedish"),
        gr.Audio(label="Import an audio", source="upload", type="numpy"),
        gr.Audio(label="Record an audio", source="microphone", type="numpy"),
    ],
    outputs=[
        gr.Text(label="Translation"),
    ],
    title=title,
    description=description,
    article="",
    examples=[],
).launch()


demo.launch()