Update app.py
Browse files
app.py
CHANGED
@@ -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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@@ -60,8 +60,14 @@ 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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# ------------------------------------------------------
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@@ -69,7 +75,7 @@ tts_config = {
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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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@@ -110,31 +116,31 @@ 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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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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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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@@ -147,8 +153,8 @@ def run_melo_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
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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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@@ -180,8 +186,8 @@ 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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@@ -212,14 +218,12 @@ 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
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"3.
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"
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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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from transformers import pipeline, VitsModel, AutoTokenizer
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from datasets import load_dataset
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# For Coqui TTS (XTTS-v2)
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try:
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from TTS.api import TTS as CoquiTTS
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except ImportError:
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raise ImportError("Please install Coqui TTS via pip install TTS.")
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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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# - Coqui XTTS-v2 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": "coqui"},
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"Japanese": {"type": "coqui"}
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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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# ------------------------------------------------------
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translator_cache = {}
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mms_tts_cache = {} # For MMS (VITS-based) TTS models
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coqui_tts_cache = None # Single instance for Coqui XTTS-v2
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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. Coqui TTS Helper for Chinese and Japanese
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# ------------------------------------------------------
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def load_coqui_tts():
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global coqui_tts_cache
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if coqui_tts_cache is not None:
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return coqui_tts_cache
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try:
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# Set gpu=True if a GPU is available.
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coqui_tts_cache = CoquiTTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=False)
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except Exception as e:
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raise RuntimeError(f"Failed to load Coqui XTTS-v2 TTS: {e}")
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return coqui_tts_cache
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def run_coqui_tts(text, lang):
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coqui_tts = load_coqui_tts()
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lang_code = coqui_lang_map[lang] # "zh" for Chinese or "ja" for Japanese
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# Write the output to a temporary file and then read it back.
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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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coqui_tts.tts_to_file(
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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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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 Coqui XTTS-v2.
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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 == "coqui":
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sr, waveform = run_coqui_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 models.\n"
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"3. Provides Synthetic speech:\n"
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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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