Hugging Face's logo

Spaces:
testingfaces
/
clearwave-ai
Paused

App Files Community
2
clearwave-ai
File size: 5,086 Bytes
9ea4293
 
7c3a40a
6f5e85f
7c3a40a
 
933850f
 
 
 
 
 
7c3a40a
 
933850f
7c3a40a
933850f
b14d6e4
 
 
 
dde471f
 
 
 
933850f
b14d6e4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
import sys
import types
_a = types.ModuleType('audioop')
_a.ratecv = lambda *a, **k: (b'', 0)
sys.modules['audioop'] = _a
sys.modules['pyaudioop'] = _a

import gradio as gr
import os
import time
import tempfile
import shutil
import subprocess
import numpy as np

print("ClearWave AI starting...")

# ── Services (flat structure - all files in root) ────────────────────
from denoiser    import Denoiser
from transcriber import Transcriber
from translator  import Translator

_denoiser    = Denoiser()
_transcriber = Transcriber()
_translator  = Translator()

# ── Config ───────────────────────────────────────────────────────────
INPUT_LANGS  = ["Auto Detect", "English", "Telugu", "Hindi", "Tamil", "Kannada"]
OUTPUT_LANGS = ["Telugu", "Hindi", "Tamil", "English", "Kannada"]
LANG_CODES   = {
    "Auto Detect": "auto",
    "English":     "en",
    "Telugu":      "te",
    "Hindi":       "hi",
    "Tamil":       "ta",
    "Kannada":     "kn",
}

# ── Pipeline ─────────────────────────────────────────────────────────
def process(audio_path, in_lang_label, out_lang_label, progress=gr.Progress()):
    if audio_path is None:
        return None, "Please upload audio.", "", "", "No audio"

    in_lang  = LANG_CODES.get(in_lang_label, "auto")
    out_lang = LANG_CODES.get(out_lang_label, "te")
    tmp      = tempfile.mkdtemp()
    t_total  = time.time()

    try:
        # Dept 1 — Denoise
        progress(0.1, desc="Dept 1: Denoising...")
        t0    = time.time()
        clean = _denoiser.process(audio_path, tmp)
        t1    = time.time() - t0

        # Dept 2 — Transcribe
        progress(0.4, desc="Dept 2: Transcribing...")
        t0                          = time.time()
        transcript, detected, tx_m  = _transcriber.transcribe(clean, in_lang)
        t2                          = time.time() - t0

        # Dept 3 — Translate
        progress(0.75, desc="Dept 3: Translating...")
        src          = detected if in_lang == "auto" else in_lang
        t0           = time.time()
        translated, tr_m = _translator.translate(transcript, src, out_lang)
        t3           = time.time() - t0

        total = time.time() - t_total
        progress(1.0, desc=f"Done in {total:.1f}s!")

        timing = (
            f"| Step | Time | Method |\n|---|---|---|\n"
            f"| Denoise    | {t1:.1f}s | noisereduce |\n"
            f"| Transcribe | {t2:.1f}s | {tx_m} |\n"
            f"| Translate  | {t3:.1f}s | {tr_m} |\n"
            f"| **Total**  | **{total:.1f}s** | |"
        )

        out_audio = os.path.join(tmp, "output.wav")
        shutil.copy(clean, out_audio)
        return out_audio, transcript, translated, timing, f"Done in {total:.1f}s"

    except Exception as e:
        import traceback
        return None, f"Error: {e}", "", traceback.format_exc(), "Failed"


# ── UI ───────────────────────────────────────────────────────────────
with gr.Blocks(title="ClearWave AI", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎵 ClearWave AI\n**Denoise · Transcribe · Translate**")

    with gr.Row():
        with gr.Column(scale=1):
            audio_in = gr.Audio(
                label="Upload Audio",
                type="filepath",
                sources=["upload", "microphone"],
            )
            in_lang  = gr.Dropdown(INPUT_LANGS,  value="Auto Detect", label="Input Language")
            out_lang = gr.Dropdown(OUTPUT_LANGS, value="Telugu",      label="Output Language")
            run_btn  = gr.Button("Process Audio", variant="primary", size="lg")
            status   = gr.Markdown("Upload audio and click Process.")

        with gr.Column(scale=2):
            with gr.Tabs():
                with gr.Tab("Text"):
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown("#### Transcript")
                            transcript_out = gr.Markdown("...")
                        with gr.Column():
                            gr.Markdown("#### Translation")
                            translation_out = gr.Markdown("...")
                with gr.Tab("Clean Audio"):
                    audio_out = gr.Audio(
                        label="Denoised",
                        type="filepath",
                        interactive=False,
                    )
                with gr.Tab("Timings"):
                    timing_out = gr.Markdown("...")

    run_btn.click(
        fn=process,
        inputs=[audio_in, in_lang, out_lang],
        outputs=[audio_out, transcript_out, translation_out, timing_out, status],
        show_progress=True,
        api_name=False,
    )

print("ClearWave AI ready!")
demo.launch()