feat(HF space): adds auto phonemization of the word.

common.py

@@ -1,3 +1,5 @@
+import enum
+
 import numpy as np
 import torch
 import torchaudio
@@ -6,6 +8,9 @@ import wavlm_phoneme_fr_it
 
 SAMPLING_RATE = 16_000
 
+class Languages(enum.Enum):
+    FR = 0
+    IT = 1
 
 def get_model():
     checkpoint = "hugofara/wavlm-base-plus-phonemizer-fr-it"
@@ -44,7 +49,7 @@ def preprocess_audio(audio_data, target_sample_rate=SAMPLING_RATE):
     return audio
 
 
-def prepare_model_inputs(audio, processor, sampling_rate=SAMPLING_RATE):
+def prepare_model_inputs(audio, processor, sampling_rate=SAMPLING_RATE, language=Languages.FR):
     """Prepare inputs for the model"""
     inputs = processor(
         audio,
@@ -54,7 +59,8 @@ def prepare_model_inputs(audio, processor, sampling_rate=SAMPLING_RATE):
     )
 
     # Add language tensor (assuming French/Italian model)
-    inputs["language"] = torch.tensor([[0]])
+    language_code = 0. if language is Languages.FR else 1.
+    inputs["language"] = torch.tensor([[language_code]], dtype=torch.float32)
 
     return inputs
 

grapheme_to_phoneme.py

@@ -0,0 +1,152 @@
+"""
+Customized phonemization wrapper.
+Original wrapper by Lingjuan Zhu (@lingjzhu):
+https://github.com/lingjzhu/text2phonemesequence
+
+"""
+import csv
+import json
+import os
+import re
+import subprocess
+import warnings
+
+import segments
+import torch
+import transformers
+
+
+class Grapheme2Phoneme:
+    def __init__(
+        self,
+        words_to_exclude=None,
+        tokenizer="google/byt5-small",
+        language="fr",
+        cuda=True,
+        folder_language="lang_dict",
+    ):
+        """
+        Load the rules to convert from a language to the corresponding phonemes.
+
+        :param list words_to_exclude: List of words to exclude from phonemization.
+        :param str tokenizer: Pre-trained tokenizer model name.
+        :param str language: Language code for phonemization. Two letters only (e.g.: "fr")
+        :param bool cuda: Flag to use CUDA for GPU acceleration.
+        :param str folder_language: Folder path to save language dictionaries.
+        """
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer)
+        self.model = transformers.T5ForConditionalGeneration.from_pretrained(
+            "charsiu/g2p_multilingual_byT5_small_100"
+        )
+        device = "cpu"
+        if cuda:
+            if torch.cuda.is_available():
+                device = "cuda:0"
+            else:
+                warnings.warn("CUDA is not available but was requested")
+        self.device = torch.device(device)
+        self.model = self.model.to(self.device)
+        if words_to_exclude is None:
+            words_to_exclude = ["[UNK]"]
+        self.exclude_token = words_to_exclude
+        self.segment_tool = segments.Tokenizer()
+        self.language = {
+            "fr": "fra",
+            "it": "ita"
+        }[language]
+        self.phone_dict = {}
+        # Download language dictionary if not exists
+        os.makedirs(folder_language, exist_ok=True)
+        language_path = os.path.join(folder_language, self.language + ".tsv")
+        if not os.path.exists(language_path):
+            subprocess.run(
+                [
+                    "wget",
+                    "-O",
+                    language_path,
+                    f"https://raw.githubusercontent.com/lingjzhu/CharsiuG2P/main/dicts/{self.language}.tsv"
+                ],
+                check=True
+            )
+        # Setting phonemes length
+        config_path = "configs/phoneme_lengths.json"
+        if os.path.exists(config_path):
+            with open(config_path, "r", encoding="utf-8") as file:
+                self.phoneme_lengths = json.load(file)
+        else:
+            warnings.warn("Loading dummy values for phonemes lengths!")
+            self.phoneme_lengths = {
+                "fra.tsv": 50,
+                "ita.tsv": 50
+            }
+        if os.path.exists(language_path):
+            with open(language_path, "r", encoding="utf-8") as file:
+                tsv_file = csv.reader(file, delimiter="\t")
+                # printing data line by line
+                for word_phonemes in tsv_file:
+                    assert len(word_phonemes) == 2
+                    self.phone_dict[word_phonemes[0]] = [word_phonemes[1].split(",")[0]]
+
+    def phonemize(self, words):
+        """
+        Convert text to phonemes using the T5 model.
+
+        :param list[str] words: Input text to be converted.
+        :return list[str]: Phonemized words.
+        """
+        phonemes_list = [""] * len(words)
+        new_words = []
+        for i, word in enumerate(words):
+            # First normalize the spacing around special tokens
+            word = re.sub(r"(?<!\s)\[PAD](?!\s)", r"<pad>", word)
+            word = re.sub(r"(?<!\s)\[UNK](?!\s)", r"<unk>", word)
+            # Then collapse multiple spaces
+            word = re.sub(r" +", " ", word).strip()
+            if word in self.phone_dict:
+                phonemes_list[i] = self.phone_dict[word][0]
+            elif word in self.exclude_token:
+                phonemes_list[i] = word
+            else:
+                new_words.append((i, word))
+
+        # Then batch the unknown words
+        if new_words:
+            out = self.tokenizer(
+                [f"<{self.language}>: {word[1]}" for word in new_words],
+                padding=True,
+                add_special_tokens=False,
+                return_tensors="pt",
+            )
+            predictions = self.model.generate(
+                input_ids=out["input_ids"].to(self.device),
+                attention_mask=out["attention_mask"].to(self.device),
+                num_beams=1,
+                max_length=self.phoneme_lengths[self.language + ".tsv"],
+            )
+            phonemes = self.tokenizer.batch_decode(
+                predictions, skip_special_tokens=True
+            )
+            for i, phonemized in enumerate(phonemes):
+                phonemes_list[new_words[i][0]] = phonemized
+
+        for i, phoneme in enumerate(phonemes_list):
+            # skip excluded tokens of segmentation
+            if phoneme not in self.exclude_token:
+                try:
+                    phonemes_list[i] = self.segment_tool(phoneme, ipa=True)
+                except IndexError:
+                    # Some phonemes may not be recognized
+                    phonemes_list[i] = self.segment_tool(phoneme)
+
+        # fill gaps with padding token
+        return phonemes_list
+
+
+if __name__ == "__main__":
+    phonemizer = Grapheme2Phoneme(language="it", cuda=False)
+    print(phonemizer.phonemize([
+        "cuore, prezzemolo", "margherita, girotondo",
+        "mepola, rofe", "presemma, costro",
+        "lada, vufebu", "nurto, frimace",
+        "banzione, ceglirate", "banzione, alcelisto"
+    ]))

hf_space.py

@@ -1,22 +1,43 @@
 import gradio as gr
 
 import common
+from grapheme_to_phoneme import Grapheme2Phoneme
 
 model, processor = common.get_model()
 
+# Initialize phonemizers for both languages
+phonemizer_fr = Grapheme2Phoneme(language="fr", cuda=False)
+phonemizer_it = Grapheme2Phoneme(language="it", cuda=False)
 
-def process_audio(audio_data, target_word):
+
+def phonemize_text(text, language):
+    """Convert text to phonemes using the appropriate phonemizer"""
+    if not text or not text.strip():
+        return ""
+
+    phonemizer = phonemizer_fr if language == "French" else phonemizer_it
+    phonemes = phonemizer.phonemize([text.strip()])
+    return " ".join([word.replace(" ", "") for word in phonemes]) if phonemes and phonemes[0] else ""
+
+
+def process_audio(audio_data, target_word, language):
     """Process recorded audio and return ASR output with target word comparison"""
     if audio_data is None:
-        return "Please record some audio first."
+        return "Please record some audio first.", ""
+
+    # Convert target word to phonemes if provided
+    phonemized_target = ""
+    if target_word and target_word.strip():
+        phonemized_target = phonemize_text(target_word, language)
 
     # Preprocess audio
     audio = common.preprocess_audio(audio_data)
     if audio is None:
-        return "Failed to process audio."
+        return "Failed to process audio.", ""
 
-    # Prepare model inputs
-    inputs = common.prepare_model_inputs(audio, processor)
+    # Prepare model inputs with correct language
+    lang_enum = common.Languages.FR if language == "French" else common.Languages.IT
+    inputs = common.prepare_model_inputs(audio, processor, language=lang_enum)
 
     # Run inference
     outputs, predicted_ids = common.run_inference(model, inputs)
@@ -24,8 +45,24 @@ def process_audio(audio_data, target_word):
     # Decode transcription
     transcription = common.decode_transcription(processor, predicted_ids)
 
-    # Compare with target word
-    return common.compare_with_target(transcription, target_word)
+    # Create detailed result
+    result = f"**Language:** {language}\n\n"
+    result += f"**Transcription:** {transcription}\n\n"
+
+    if target_word and target_word.strip():
+        result += f"**Target Word:** {target_word}\n"
+        result += f"**Target Phonemes:** {phonemized_target}\n\n"
+
+        # Compare phonemized versions
+        transcription_clean = transcription.lower().replace("[pad]", "").strip()
+        phonemized_target_clean = phonemized_target.lower().strip()
+
+        if phonemized_target_clean in transcription_clean:
+            result += f"✅ **Phoneme Match!** The phonemized target appears in the transcription."
+        else:
+            result += f"❌ **No phoneme match.** The phonemized target was not found in the transcription."
+
+    return result, phonemized_target
 
 
 def create_interface():
@@ -37,10 +74,24 @@ def create_interface():
 
         with gr.Row():
             with gr.Column():
+                language_radio = gr.Radio(
+                    choices=["French", "Italian"],
+                    value="French",
+                    label="Model Language",
+                    info="Select the language for ASR recognition"
+                )
+
                 target_word_input = gr.Textbox(
                     label="Target Word (optional)",
                     placeholder="Enter a word you expect to say...",
-                    info="Leave empty to just get the transcription"
+                    info="Will be converted to phonemes for comparison"
+                )
+
+                phonemes_display = gr.Textbox(
+                    label="Target Phonemes",
+                    interactive=False,
+                    placeholder="Phonemes will appear here...",
+                    info="Automatic phoneme conversion of your target word"
                 )
 
                 audio_input = gr.Audio(
@@ -53,21 +104,38 @@ def create_interface():
 
             with gr.Column():
                 output_text = gr.Markdown(
-                    label="Results",
                     value="Results will appear here after processing..."
                 )
 
+        # Update phonemes when target word or language changes
+        def update_phonemes(text, language):
+            if text and text.strip():
+                return phonemize_text(text, language)
+            return ""
+
+        target_word_input.change(
+            fn=update_phonemes,
+            inputs=[target_word_input, language_radio],
+            outputs=phonemes_display
+        )
+
+        language_radio.change(
+            fn=update_phonemes,
+            inputs=[target_word_input, language_radio],
+            outputs=phonemes_display
+        )
+
         process_btn.click(
             fn=process_audio,
-            inputs=[audio_input, target_word_input],
-            outputs=output_text
+            inputs=[audio_input, target_word_input, language_radio],
+            outputs=[output_text, phonemes_display]
         )
 
         # Auto-process when audio is recorded
         audio_input.change(
             fn=process_audio,
-            inputs=[audio_input, target_word_input],
-            outputs=output_text
+            inputs=[audio_input, target_word_input, language_radio],
+            outputs=[output_text, phonemes_display]
         )
 
     return demo

pyproject.toml

@@ -9,6 +9,7 @@ dependencies = [
     "matplotlib>=3.10.6",
     "numpy>=2.3.3",
     "pyqt6>=6.9.1",
+    "segments>=2.3.0",
     "sounddevice>=0.5.2",
     "torch>=2.8.0",
     "torchaudio>=2.8.0",

uv.lock

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+    { name = "jsonschema" },
+    { name = "language-tags" },
+    { name = "python-dateutil" },
+    { name = "rdflib" },
+    { name = "requests" },
+    { name = "rfc3986" },
+    { name = "termcolor" },
+    { name = "uritemplate" },
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+    { url = "https://files.pythonhosted.org/packages/a9/99/3ae339466c9183ea5b8ae87b34c0b897eda475d2aec2307cae60e5cd4f29/uritemplate-4.2.0-py3-none-any.whl", hash = "sha256:962201ba1c4edcab02e60f9a0d3821e82dfc5d2d6662a21abd533879bdb8a686", size = 11488, upload-time = "2025-06-02T15:12:03.405Z" },
+]
+
 [[package]]
 name = "urllib3"
 version = "2.5.0"
@@ -1780,6 +2012,7 @@ dependencies = [
     { name = "matplotlib" },
     { name = "numpy" },
     { name = "pyqt6" },
+    { name = "segments" },
     { name = "sounddevice" },
     { name = "torch" },
     { name = "torchaudio" },
@@ -1793,6 +2026,7 @@ requires-dist = [
     { name = "matplotlib", specifier = ">=3.10.6" },
     { name = "numpy", specifier = ">=2.3.3" },
     { name = "pyqt6", specifier = ">=6.9.1" },
+    { name = "segments", specifier = ">=2.3.0" },
     { name = "sounddevice", specifier = ">=0.5.2" },
     { name = "torch", specifier = ">=2.8.0" },
     { name = "torchaudio", specifier = ">=2.8.0" },