Update app.py
Browse files
app.py
CHANGED
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@@ -9,11 +9,11 @@ import os
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from transformers import pipeline, VitsModel, AutoTokenizer
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from datasets import load_dataset
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# For
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try:
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from
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except ImportError:
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raise ImportError("Please install
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# ------------------------------------------------------
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# 1. ASR Pipeline (English) using Wav2Vec2
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@@ -51,7 +51,7 @@ translation_tasks = {
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# ------------------------------------------------------
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# 3. TTS Configuration
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# - MMS TTS (VITS) for: Spanish, Vietnamese, Indonesian, Turkish, Portuguese, Korean
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# -
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# ------------------------------------------------------
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tts_config = {
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"Spanish": {"model_id": "facebook/mms-tts-spa", "architecture": "vits", "type": "mms"},
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@@ -60,14 +60,8 @@ tts_config = {
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"Turkish": {"model_id": "facebook/mms-tts-tur", "architecture": "vits", "type": "mms"},
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"Portuguese": {"model_id": "facebook/mms-tts-por", "architecture": "vits", "type": "mms"},
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"Korean": {"model_id": "facebook/mms-tts-kor", "architecture": "vits", "type": "mms"},
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"Chinese": {"type": "
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"Japanese": {"type": "
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}
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# For Coqui, we map our languages to language codes expected by the model.
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coqui_lang_map = {
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"Chinese": "zh",
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"Japanese": "ja"
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}
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# ------------------------------------------------------
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@@ -75,7 +69,7 @@ coqui_lang_map = {
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# ------------------------------------------------------
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translator_cache = {}
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mms_tts_cache = {} # For MMS (VITS-based) TTS models
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# ------------------------------------------------------
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# 5. Translator Helper
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@@ -116,31 +110,31 @@ def run_mms_tts(text, lang):
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return sample_rate, waveform
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# ------------------------------------------------------
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# 7.
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# ------------------------------------------------------
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def
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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tmp_name = tmp.name
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try:
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text=text,
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file_path=tmp_name,
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language=lang_code # using default voice; for cloning, add speaker_wav parameter
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)
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data, sr = sf.read(tmp_name)
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finally:
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if os.path.exists(tmp_name):
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@@ -153,8 +147,8 @@ def run_coqui_tts(text, lang):
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def predict(audio, text, target_language):
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"""
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1. Obtain English text (via ASR if audio provided, else text).
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2. Translate English text to target_language.
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3. Generate TTS audio using either MMS TTS (VITS) or
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"""
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# Step 1: Get English text.
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if text.strip():
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@@ -186,8 +180,8 @@ def predict(audio, text, target_language):
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tts_type = tts_config[target_language]["type"]
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if tts_type == "mms":
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sr, waveform = run_mms_tts(translated_text, target_language)
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elif tts_type == "
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sr, waveform =
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else:
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raise RuntimeError("Unknown TTS type for target language.")
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except Exception as e:
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@@ -218,12 +212,14 @@ iface = gr.Interface(
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description=(
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"This app performs the following steps:\n"
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"1. Transcribes English speech using Wav2Vec2 (or accepts text input).\n"
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"2. Translates the English text to the target language using Helsinki-NLP models.\n"
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"3.
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"For Spanish, Vietnamese, Indonesian, Turkish, Portuguese, and Korean
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),
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allow_flagging="never"
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)
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if __name__ == "__main__":
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iface.launch(server_name="0.0.0.0", server_port=7860)
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from transformers import pipeline, VitsModel, AutoTokenizer
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from datasets import load_dataset
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# For MeloTTS (Chinese and Japanese)
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try:
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from melo.api import TTS as MeloTTS
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except ImportError:
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raise ImportError("Please install the MeloTTS package (e.g., pip install myshell-ai/MeloTTS-Chinese)")
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# ------------------------------------------------------
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# 1. ASR Pipeline (English) using Wav2Vec2
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# ------------------------------------------------------
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# 3. TTS Configuration
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# - MMS TTS (VITS) for: Spanish, Vietnamese, Indonesian, Turkish, Portuguese, Korean
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# - MeloTTS for: Chinese and Japanese
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# ------------------------------------------------------
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tts_config = {
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"Spanish": {"model_id": "facebook/mms-tts-spa", "architecture": "vits", "type": "mms"},
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"Turkish": {"model_id": "facebook/mms-tts-tur", "architecture": "vits", "type": "mms"},
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"Portuguese": {"model_id": "facebook/mms-tts-por", "architecture": "vits", "type": "mms"},
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"Korean": {"model_id": "facebook/mms-tts-kor", "architecture": "vits", "type": "mms"},
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"Chinese": {"type": "melo"},
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"Japanese": {"type": "melo"}
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}
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# ------------------------------------------------------
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# ------------------------------------------------------
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translator_cache = {}
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mms_tts_cache = {} # For MMS (VITS-based) TTS models
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melo_tts_cache = {} # For MeloTTS models (Chinese/Japanese)
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# ------------------------------------------------------
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# 5. Translator Helper
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return sample_rate, waveform
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# ------------------------------------------------------
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# 7. MeloTTS Helper for Chinese and Japanese
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# ------------------------------------------------------
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def run_melo_tts(text, lang):
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"""
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Uses the myshell-ai MeloTTS model.
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For Chinese, use language parameter 'ZH'; for Japanese, use 'JP'.
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"""
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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lang_param = 'ZH' if lang == "Chinese" else 'JP'
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if lang not in melo_tts_cache:
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try:
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model = MeloTTS(language=lang_param, device=device)
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melo_tts_cache[lang] = model
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except Exception as e:
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raise RuntimeError(f"Failed to load MeloTTS model for {lang}: {e}")
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else:
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model = melo_tts_cache[lang]
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speaker_ids = model.hps.data.spk2id
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# Assume the speaker key is the same as lang_param
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speaker_key = lang_param
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speed = 1.0
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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tmp_name = tmp.name
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try:
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model.tts_to_file(text, speaker_ids[speaker_key], tmp_name, speed=speed)
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data, sr = sf.read(tmp_name)
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finally:
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if os.path.exists(tmp_name):
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def predict(audio, text, target_language):
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"""
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1. Obtain English text (via ASR if audio provided, else text).
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2. Translate the English text to target_language.
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3. Generate TTS audio using either MMS TTS (VITS) or MeloTTS.
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"""
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# Step 1: Get English text.
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if text.strip():
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tts_type = tts_config[target_language]["type"]
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if tts_type == "mms":
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sr, waveform = run_mms_tts(translated_text, target_language)
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elif tts_type == "melo":
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sr, waveform = run_melo_tts(translated_text, target_language)
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else:
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raise RuntimeError("Unknown TTS type for target language.")
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except Exception as e:
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description=(
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"This app performs the following steps:\n"
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"1. Transcribes English speech using Wav2Vec2 (or accepts text input).\n"
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"2. Translates the English text to the target language using Helsinki-NLP MarianMT models.\n"
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"3. Synthesizes speech:\n"
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" - For Spanish, Vietnamese, Indonesian, Turkish, Portuguese, and Korean: uses Facebook MMS TTS (VITS-based).\n"
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" - For Chinese and Japanese: uses myshell-ai MeloTTS models.\n"
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"\nSelect your target language from the dropdown."
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),
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allow_flagging="never"
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)
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if __name__ == "__main__":
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iface.launch(server_name="0.0.0.0", server_port=7860)
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