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import tempfile |
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import gradio as gr |
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import os |
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from TTS.utils.synthesizer import Synthesizer |
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from espeak_phonemizer import Phonemizer |
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from engine import Piper |
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from festival import festival_synthesize |
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from mms import MMS |
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MAX_TXT_LEN = 325 |
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fonemitzador = Phonemizer("ca") |
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def carrega_bsc(): |
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model_path = os.getcwd() + "/models/bsc/best_model.pth" |
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config_path = os.getcwd() + "/models/bsc/config.json" |
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speakers_file_path = os.getcwd() + "/models/bsc/speakers.pth" |
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vocoder_path = None |
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vocoder_config_path = None |
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synthesizer = Synthesizer( |
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model_path, config_path, speakers_file_path, None, vocoder_path, vocoder_config_path, |
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) |
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return synthesizer |
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def carrega_collectivat(): |
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model_path = os.getcwd() + "/models/collectivat/fast-speech_best_model.pth" |
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config_path = os.getcwd() + "/models/collectivat/fast-speech_config.json" |
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vocoder_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_model_file.pth" |
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vocoder_config_path = os.getcwd() + "/models/collectivat/ljspeech--hifigan_v2_config.json" |
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synthesizer = Synthesizer( |
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model_path, config_path, None, None, vocoder_path, vocoder_config_path |
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) |
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return synthesizer |
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def carrega_piper(): |
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return Piper(os.getcwd() + "/models/piper/ca-upc_ona-x-low.onnx") |
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def carrega_mms(): |
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return MMS(os.getcwd() + "/models/mms") |
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model_bsc = carrega_bsc() |
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SPEAKERS = model_bsc.tts_model.speaker_manager.speaker_names |
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model_collectivat = carrega_collectivat() |
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model_piper = carrega_piper() |
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model_mms = carrega_mms() |
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request_count = 0 |
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def tts(text, festival_voice, speaker_idx): |
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if len(text) > MAX_TXT_LEN: |
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text = text[:MAX_TXT_LEN] |
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print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.") |
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print(text) |
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wav_bsc = model_bsc.tts(text, speaker_idx) |
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wav_coll = model_collectivat.tts(text) |
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wav_piper = model_piper.synthesize(text) |
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fp_bsc = "" |
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: |
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model_bsc.save_wav(wav_bsc, fp) |
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fp_bsc = fp.name |
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fp_coll = "" |
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: |
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model_collectivat.save_wav(wav_coll, fp) |
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fp_coll = fp.name |
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fp_piper = "" |
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: |
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fp.write(wav_piper) |
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fp_piper = fp.name |
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fp_mms = "" |
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: |
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model_mms.synthesize(fp.name, text) |
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fp_mms = fp.name |
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fonemes = fonemitzador.phonemize(text, keep_clause_breakers=True) |
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fp_festival = festival_synthesize(text, festival_voice) |
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global request_count |
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request_count += 1 |
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print(f"Requests: {request_count}") |
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return fonemes, fp_festival, fp_bsc, fp_coll, fp_piper, fp_mms |
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description=""" |
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Amb aquesta aplicació podeu sintetitzar text a veu amb alguns models neuronals lliures pel català i amb el motor Festival. |
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1. Model multi-parlant VITS entrenat pel BSC (Projecte Aina) [enllaç](https://huggingface.co/projecte-aina/tts-ca-coqui-vits-multispeaker) |
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2. Model Fastspeech entrenat per Col·lectivat [enllaç](https://github.com/CollectivaT-dev/TTS-API) |
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3. Model VITS entrenat per Piper/Home Assistant [enllaç](https://github.com/rhasspy/piper) |
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3. Model VITS entrenat per Meta (llicència CC-BY-NC) [enllaç](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) |
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El primer model ha estat entrenat amb totes les veus de FestCAT, els talls de Common Voice 8 i un altre corpus pel que conté moltes veus de qualitat variable. La veu d'Ona està seleccionada per defecte per la comparativa però podeu provar les altres. |
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Els models 2 i 3 han estat entrenats amb la veu d'Ona de FestCAT. |
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El model 4, anomenat MMS, de Meta (Facebook) ha estat entrenat a partir de dades d'un [audiollibre](http://live.bible.is/bible/CATBSS/LUK/1) de la Bíblia |
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Aquesta aplicació fa servir l'últim estat de l'espeak millorat per Carme Armentano del BSC |
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https://github.com/projecte-aina/espeak-ng |
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NOTA: El model de col·lectivat treballa amb grafemes pel que no fa servir espeak com a fonemitzador. Festival conté les seves pròpies normes fonètiques. |
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ACTUALITZACIÖ (Agost 2024): El BSC en el marc del projecte Aina ha publicat nous models, podeu provar-los [aquí](https://huggingface.co/spaces/projecte-aina/matxa-alvocat-tts-ca). |
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""" |
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article= "" |
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iface = gr.Interface( |
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fn=tts, |
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inputs=[ |
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gr.Textbox( |
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label="Text", |
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value="L'Èlia i l'Alí a l'aula. L'oli i l'ou. Lulú olorava la lila.", |
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), |
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gr.Dropdown(label="Parlant del motor Festival", choices=["ona", "pau"], value="ona"), |
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gr.Dropdown(label="Parlant del model VITS multi-parlant del BSC", choices=SPEAKERS, value="ona") |
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], |
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outputs=[ |
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gr.Markdown(label="Fonemes"), |
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gr.Audio(label="Festival",type="filepath"), |
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gr.Audio(label="BSC VITS",type="filepath"), |
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gr.Audio(label="Collectivat Fastspeech",type="filepath"), |
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gr.Audio(label="Piper VITS",type="filepath"), |
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gr.Audio(label="Meta MMS VITS",type="filepath") |
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], |
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title="Comparativa de síntesi lliure en català️", |
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description=description, |
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article=article, |
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allow_flagging="never", |
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layout="vertical", |
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live=False, |
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examples=[ |
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["Duc pà sec al sac, m'assec on sóc i el suco amb suc", "ona", "ona"], |
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["Un plat pla blanc, ple de pebre negre n’era. Un plat blanc pla, ple de pebre negre està", "ona", "ona"], |
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["Visc al bosc i busco vesc i visc del vesc que busco al bosc", "ona", "ona"], |
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["Una polla xica, pica, pellarica, camatorta i becarica va tenir sis polls xics, pics, pellarics, camacurts i becarics. Si la polla no hagués sigut xica, pica, pellarica, camatorta i becarica, els sis polls no haurien sigut xics, pics, pellarics, camacurts i becarics.", "ona", "ona"] |
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] |
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) |
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iface.launch(server_name="0.0.0.0", server_port=7860) |
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