File size: 6,813 Bytes
cc34edf
 
 
 
 
 
 
 
31718a6
 
 
cc34edf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31718a6
cc34edf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31718a6
cc34edf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31718a6
 
 
 
 
 
cc34edf
31718a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cc34edf
31718a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
614dc5d
 
31718a6
 
 
 
cc34edf
31718a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
04e9646
31718a6
 
 
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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
import spaces
import gradio as gr
import torch
import numpy as np
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import platform
import librosa

processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
model.to('cuda')

class PhoneticEnhancer:
    def __init__(self):
        # Vowel length rules
        self.long_vowels = {
            'i': 'iː',
            'u': 'uː',
            'a': 'ɑː',
            'ɑ': 'ɑː',
            'e': 'eː',
            'o': 'oː'
        }
        
        # Common diphthongs
        self.diphthongs = {
            'ei': 'eɪ',
            'ai': 'aɪ',
            'oi': 'ɔɪ',
            'ou': 'əʊ',
            'au': 'aʊ'
        }
        
        # Vowel quality adjustments
        self.vowel_quality = {
            'ə': 'æ',  # In stressed positions
            'ɐ': 'æ'   # Common substitution
        }
        
        # Stress pattern rules
        self.stress_patterns = [
            # (pattern, position) - position is index from start
            (['CV', 'CV'], 1),  # For words like "piage"
            (['CVV', 'CV'], 0), # For words with long first vowel
        ]
    
    def _is_vowel(self, phoneme: str) -> bool:
        vowels = set('aeiouɑɐəæɛɪʊʌɔ')
        return any(char in vowels for char in phoneme)
    
    def _split_into_syllables(self, phonemes: list) -> list:
        syllables = []
        current_syllable = []
        
        for phoneme in phonemes:
            current_syllable.append(phoneme)
            if self._is_vowel(phoneme) and len(current_syllable) > 0:
                syllables.append(current_syllable)
                current_syllable = []
        
        if current_syllable:
            if len(syllables) > 0:
                syllables[-1].extend(current_syllable)
            else:
                syllables.append(current_syllable)
        
        return syllables

    def enhance_transcription(self, raw_phonemes: str, enhancements: list = None) -> str:
        if enhancements is None:
            enhancements = ['length', 'quality', 'stress', 'diphthongs']
        
        # Split into individual phonemes
        phonemes = raw_phonemes.split()
        enhanced_phonemes = phonemes.copy()
        
        if 'length' in enhancements:
            # Apply vowel length rules
            for i, phoneme in enumerate(enhanced_phonemes):
                if phoneme in self.long_vowels:
                    enhanced_phonemes[i] = self.long_vowels[phoneme]
        
        if 'quality' in enhancements:
            # Apply vowel quality adjustments
            for i, phoneme in enumerate(enhanced_phonemes):
                if phoneme in self.vowel_quality:
                    enhanced_phonemes[i] = self.vowel_quality[phoneme]
        
        if 'diphthongs' in enhancements:
            # Apply diphthong rules
            i = 0
            while i < len(enhanced_phonemes) - 1:
                pair = enhanced_phonemes[i] + enhanced_phonemes[i + 1]
                if pair in self.diphthongs:
                    enhanced_phonemes[i] = self.diphthongs[pair]
                    enhanced_phonemes.pop(i + 1)
                i += 1
        
        if 'stress' in enhancements:
            # Add stress marks based on syllable structure
            syllables = self._split_into_syllables(enhanced_phonemes)
            if len(syllables) > 1:
                # Add stress to the syllable containing 'æ' if present
                for i, syll in enumerate(syllables):
                    if any('æ' in p for p in syll):
                        syllables[i].insert(0, 'ˈ')
                        break
                # If no 'æ', add stress to first syllable by default
                else:
                    syllables[0].insert(0, 'ˈ')
            
            # Flatten syllables back to phonemes
            enhanced_phonemes = [p for syll in syllables for p in syll]
        
        return ' '.join(enhanced_phonemes)

def preprocess_audio(audio):
    """Preprocess audio data for model input."""
    if isinstance(audio, tuple):
        sample_rate, audio_data = audio
    else:
        return None
    
    if audio_data.dtype != np.float32:
        audio_data = audio_data.astype(np.float32)
    
    if audio_data.max() > 1.0 or audio_data.min() < -1.0:
        audio_data = audio_data / 32768.0
    
    if len(audio_data.shape) > 1:
        audio_data = audio_data.mean(axis=1)
    
    if sample_rate != 16000:
        audio_data = librosa.resample(
            y=audio_data,
            orig_sr=sample_rate,
            target_sr=16000
        )
    
    return audio_data

@spaces.GPU
def transcribe_to_phonemes(audio, enhancements):
    """Transcribe audio to phonemes with enhancements."""
    try:
        audio_data = preprocess_audio(audio)
        if audio_data is None:
            return "Please provide valid audio input"
        
        selected_enhancements = enhancements.split(',') if enhancements else []
        inputs = processor(
            audio_data,
            sampling_rate=16000,
            return_tensors="pt",
            padding=True
        ).input_values.to('cuda')
        
        with torch.no_grad():
            logits = model(inputs).logits
        
        predicted_ids = torch.argmax(logits, dim=-1)
        transcription = processor.batch_decode(predicted_ids)[0]
        
        enhancer = PhoneticEnhancer()
        enhanced = enhancer.enhance_transcription(
            transcription,
            selected_enhancements
        )
        
        return f"""Raw IPA: {transcription}
Enhanced IPA: {enhanced}
Applied enhancements: {', '.join(selected_enhancements) or 'none'}"""
        
    except Exception as e:
        import traceback
        return f"Error processing audio: {str(e)}\n{traceback.format_exc()}"

iface = gr.Interface(
    fn=transcribe_to_phonemes,
    inputs=[
        gr.Audio(sources=["microphone", "upload"], type="numpy"),
        gr.Textbox(
            label="Enhancements (comma-separated)", 
            value="length,quality,stress,diphthongs",
            placeholder="e.g., length,quality,stress,diphthongs"
        )
    ],
    outputs="text",
    title="Speech to Phoneme Converter - Enhanced IPA",
    description="""Convert speech to phonemes with customizable IPA enhancements.
                   
                   Available enhancements:
                   - length: Add vowel length markers (ː)
                   - quality: Adjust vowel quality (e.g., ə → æ)
                   - stress: Add stress marks (ˈ)
                   - diphthongs: Combine vowels into diphthongs (e.g., ei → eɪ)
                                      """
)

iface.launch()