(wip)replace space

tts.py

@@ -1,18 +1,5 @@
-# TODO: V2 of TTS Router
-# Currently just use current TTS router.
 import os
-import json
 from dotenv import load_dotenv
-import fal_client
-import requests
-import time
-import io
-
-from gradio_client import handle_file
-from pyht import Client as PyhtClient
-from pyht.client import TTSOptions
-import base64
-import tempfile
 import random
 
 load_dotenv()
@@ -25,54 +12,14 @@ def get_zerogpu_token():
 
 
 model_mapping = {
-    # "eleven-multilingual-v2": {
-    #     "provider": "elevenlabs",
-    #     "model": "eleven_multilingual_v2",
-    # },
-    # "eleven-turbo-v2.5": {
-    #     "provider": "elevenlabs",
-    #     "model": "eleven_turbo_v2_5",
-    # },
-    # "eleven-flash-v2.5": {
-    #     "provider": "elevenlabs",
-    #     "model": "eleven_flash_v2_5",
-    # },
     "spark-tts": {
         "provider": "spark",
         "model": "spark-tts",
     },
-    # "playht-2.0": {
-    #     "provider": "playht",
-    #     "model": "PlayHT2.0",
-    # },
-    # "styletts2": {
-    #     "provider": "styletts",
-    #     "model": "styletts2",
-    # },
     "cosyvoice-2.0": {
         "provider": "cosyvoice",
         "model": "cosyvoice_2_0",
     },
-    # "papla-p1": {
-    #     "provider": "papla",
-    #     "model": "papla_p1",
-    # },
-    # "hume-octave": {
-    #     "provider": "hume",
-    #     "model": "octave",
-    # },
-    # "minimax-02-hd": {
-    #     "provider": "minimax",
-    #     "model": "speech-02-hd",
-    # },
-    # "minimax-02-turbo": {
-    #     "provider": "minimax",
-    #     "model": "speech-02-turbo",
-    # },
-    # "lanternfish-1": {
-    #     "provider": "lanternfish",
-    #     "model": "lanternfish-1",
-    # },
     "index-tts": {
         "provider": "bilibili",
         "model": "index-tts",
@@ -95,110 +42,9 @@ headers = {
 data = {"text": "string", "provider": "string", "model": "string"}
 
 
-def predict_csm(script):
-    result = fal_client.subscribe(
-        "fal-ai/csm-1b",
-        arguments={
-            # "scene": [{
-            #     "text": "Hey how are you doing.",
-            #     "speaker_id": 0
-            # }, {
-            #     "text": "Pretty good, pretty good.",
-            #     "speaker_id": 1
-            # }, {
-            #     "text": "I'm great, so happy to be speaking to you.",
-            #     "speaker_id": 0
-            # }]
-            "scene": script
-        },
-        with_logs=True,
-    )
-    return requests.get(result["audio"]["url"]).content
-
-
-def predict_playdialog(script):
-    # Initialize the PyHT client
-    pyht_client = PyhtClient(
-        user_id=os.getenv("PLAY_USERID"),
-        api_key=os.getenv("PLAY_SECRETKEY"),
-    )
-
-    # Define the voices
-    voice_1 = "s3://voice-cloning-zero-shot/baf1ef41-36b6-428c-9bdf-50ba54682bd8/original/manifest.json"
-    voice_2 = "s3://voice-cloning-zero-shot/e040bd1b-f190-4bdb-83f0-75ef85b18f84/original/manifest.json"
-
-    # Convert script format from CSM to PlayDialog format
-    if isinstance(script, list):
-        # Process script in CSM format (list of dictionaries)
-        text = ""
-        for turn in script:
-            speaker_id = turn.get("speaker_id", 0)
-            prefix = "Host 1:" if speaker_id == 0 else "Host 2:"
-            text += f"{prefix} {turn['text']}\n"
-    else:
-        # If it's already a string, use as is
-        text = script
-
-    # Set up TTSOptions
-    options = TTSOptions(
-        voice=voice_1, voice_2=voice_2, turn_prefix="Host 1:", turn_prefix_2="Host 2:"
-    )
-
-    # Generate audio using PlayDialog
-    audio_chunks = []
-    for chunk in pyht_client.tts(text, options, voice_engine="PlayDialog"):
-        audio_chunks.append(chunk)
-
-    # Combine all chunks into a single audio file
-    return b"".join(audio_chunks)
-
-
-def predict_dia(script):
-    # Convert script to the required format for Dia
-    if isinstance(script, list):
-        # Convert from list of dictionaries to formatted string
-        formatted_text = ""
-        for turn in script:
-            speaker_id = turn.get("speaker_id", 0)
-            speaker_tag = "[S1]" if speaker_id == 0 else "[S2]"
-            text = turn.get("text", "").strip().replace("[S1]", "").replace("[S2]", "")
-            formatted_text += f"{speaker_tag} {text} "
-        text = formatted_text.strip()
-    else:
-        # If it's already a string, use as is
-        text = script
-    print(text)
-    # Make a POST request to initiate the dialogue generation
-    headers = {
-        # "Content-Type": "application/json",
-        "Authorization": f"Bearer {get_zerogpu_token()}"
-    }
-
-    response = requests.post(
-        "https://mrfakename-dia-1-6b.hf.space/gradio_api/call/generate_dialogue",
-        headers=headers,
-        json={"data": [text]},
-    )
-
-    # Extract the event ID from the response
-    event_id = response.json()["event_id"]
-
-    # Make a streaming request to get the generated dialogue
-    stream_url = f"https://mrfakename-dia-1-6b.hf.space/gradio_api/call/generate_dialogue/{event_id}"
-
-    # Use a streaming request to get the audio data
-    with requests.get(stream_url, headers=headers, stream=True) as stream_response:
-        # Process the streaming response
-        for line in stream_response.iter_lines():
-            if line:
-                if line.startswith(b"data: ") and not line.startswith(b"data: null"):
-                    audio_data = line[6:]
-                    return requests.get(json.loads(audio_data)[0]["url"]).content
-
-
 def predict_index_tts(text, reference_audio_path=None):
     from gradio_client import Client, handle_file
-    client = Client("IndexTeam/IndexTTS")
+    client = Client("IndexTeam/IndexTTS",hf_token=os.getenv("HF_TOKEN"))
     if reference_audio_path:
         prompt = handle_file(reference_audio_path)
     else:
@@ -216,7 +62,7 @@ def predict_index_tts(text, reference_audio_path=None):
 
 def predict_spark_tts(text, reference_audio_path=None):
     from gradio_client import Client, handle_file
-    client = Client("thunnai/SparkTTS")
+    client = Client("kemuriririn/SparkTTS",hf_token=os.getenv("HF_TOKEN"))
     prompt_wav = None
     if reference_audio_path:
         prompt_wav = handle_file(reference_audio_path)
@@ -233,7 +79,7 @@ def predict_spark_tts(text, reference_audio_path=None):
 
 def predict_cosyvoice_tts(text, reference_audio_path=None):
     from gradio_client import Client, file, handle_file
-    client = Client("https://iic-cosyvoice2-0-5b.ms.show/")
+    client = Client("kemuriririn/CosyVoice2-0.5B",hf_token=os.getenv("HF_TOKEN"))
     if not reference_audio_path:
         raise ValueError("cosyvoice-2.0 需要 reference_audio_path")
     prompt_wav = handle_file(reference_audio_path)
@@ -246,7 +92,7 @@ def predict_cosyvoice_tts(text, reference_audio_path=None):
     prompt_text = recog_result if isinstance(recog_result, str) else str(recog_result)
     result = client.predict(
         tts_text=text,
-        mode_checkbox_group="3s极速复刻",
+        mode_checkbox_group="3s Voice Clone",
         prompt_text=prompt_text,
         prompt_wav_upload=prompt_wav,
         prompt_wav_record=prompt_wav,
@@ -304,13 +150,7 @@ def predict_tts(text, model, reference_audio_path=None):
     global client
     print(f"Predicting TTS for {model}")
     # Exceptions: special models that shouldn't be passed to the router
-    if model == "csm-1b":
-        return predict_csm(text)
-    elif model == "playdialog-1.0":
-        return predict_playdialog(text)
-    elif model == "dia-1.6b":
-        return predict_dia(text)
-    elif model == "index-tts":
+    if model == "index-tts":
         return predict_index_tts(text, reference_audio_path)
     elif model == "spark-tts":
         return predict_spark_tts(text, reference_audio_path)
@@ -321,66 +161,8 @@ def predict_tts(text, model, reference_audio_path=None):
     elif model == "gpt-sovits-v2":
         return predict_gpt_sovits_v2(text, reference_audio_path)
 
-    if not model in model_mapping:
-        raise ValueError(f"Model {model} not found")
-
-    # 构建请求体
-    payload = {
-        "text": text,
-        "provider": model_mapping[model]["provider"],
-        "model": model_mapping[model]["model"],
-    }
-    # 仅支持音色克隆的模型传递参考音色
-    supports_reference = model in [
-        "styletts2", "eleven-multilingual-v2", "eleven-turbo-v2.5", "eleven-flash-v2.5"
-    ]
-    if reference_audio_path and supports_reference:
-        with open(reference_audio_path, "rb") as f:
-            audio_bytes = f.read()
-            audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
-        # 不同模型参考音色字段不同
-        if model == "styletts2":
-            payload["reference_speaker"] = audio_b64
-        else:  # elevenlabs 系列
-            payload["reference_audio"] = audio_b64
-
-    result = requests.post(
-        url,
-        headers=headers,
-        data=json.dumps(payload),
-    )
-
-    response_json = result.json()
-
-    audio_data = response_json["audio_data"]  # base64 encoded audio data
-    extension = response_json["extension"]
-    # Decode the base64 audio data
-    audio_bytes = base64.b64decode(audio_data)
-
-    # Create a temporary file to store the audio data
-    with tempfile.NamedTemporaryFile(delete=False, suffix=f".{extension}") as temp_file:
-        temp_file.write(audio_bytes)
-        temp_path = temp_file.name
-
-    return temp_path
+    raise ValueError(f"Model {model} not found")
 
 
 if __name__ == "__main__":
-    print(
-        predict_dia(
-            [
-                {"text": "Hello, how are you?", "speaker_id": 0},
-                {"text": "I'm great, thank you!", "speaker_id": 1},
-            ]
-        )
-    )
-    # print("Predicting PlayDialog")
-    # print(
-    #     predict_playdialog(
-    #         [
-    #             {"text": "Hey how are you doing.", "speaker_id": 0},
-    #             {"text": "Pretty good, pretty good.", "speaker_id": 1},
-    #             {"text": "I'm great, so happy to be speaking to you.", "speaker_id": 0},
-    #         ]
-    #     )
-    # )
+    pass