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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+yarngpt/notebooks/audio_0c026c21-f432-4d20-a86b-899a10d9ed60.webp filter=lfs diff=lfs merge=lfs -text

yarngpt/README.md

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+# YarnGPT 🎙️
+![image/png](https://github.com/saheedniyi02/yarngpt/blob/main/notebooks%2Faudio_0c026c21-f432-4d20-a86b-899a10d9ed60.webp)
+A text-to-speech model generating natural Nigerian-accented English speech. Built on pure language modeling without external adapters.
+
+Web Url: https://yarngpt.co/
+
+## Quick Start
+
+```python
+
+!git clone https://github.com/saheedniyi02/yarngpt.git
+
+pip install outetts uroman
+
+import os
+import re
+import json
+import torch
+import inflect
+import random
+import uroman as ur
+import numpy as np
+import torchaudio
+import IPython
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from outetts.wav_tokenizer.decoder import WavTokenizer
+
+
+!wget https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml
+!gdown 1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt
+
+
+from yarngpt.audiotokenizer import AudioTokenizerV2
+
+tokenizer_path="saheedniyi/YarnGPT2"
+wav_tokenizer_config_path="/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+wav_tokenizer_model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"
+
+
+audio_tokenizer=AudioTokenizerV2(
+    tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path
+    )
+
+
+model = AutoModelForCausalLM.from_pretrained(tokenizer_path,torch_dtype="auto").to(audio_tokenizer.device)
+
+#change the text
+text="The election was won by businessman and politician, Moshood Abiola, but Babangida annulled the results, citing concerns over national security."
+
+# change the language and voice
+prompt=audio_tokenizer.create_prompt(text,lang="english",speaker_name="idera")
+
+input_ids=audio_tokenizer.tokenize_prompt(prompt)
+
+output  = model.generate(
+            input_ids=input_ids,
+            temperature=0.1,
+            repetition_penalty=1.1,
+            max_length=4000,
+            #num_beams=5,# using a beam size helps for the local languages but not english
+        )
+
+codes=audio_tokenizer.get_codes(output)
+audio=audio_tokenizer.get_audio(codes)
+IPython.display.Audio(audio,rate=24000)
+torchaudio.save(f"Sample.wav", audio, sample_rate=24000)
+
+```
+
+## Features
+
+- 🗣️ 12 preset voices (6 male, 6 female)
+- 🎯 Trained on 2000+ hours of Nigerian audio
+- 🔊 24kHz high-quality audio output
+- 🚀 Simple API for quick integration
+- 📝 Support for long-form text
+
+## Available Voices
+- Female: zainab, idera, regina, chinenye, joke, remi
+- Male: jude, tayo, umar, osagie, onye, emma
+
+## Examples
+
+Check out our [demo notebook](link-to-notebook) or listen to [sample outputs](https://huggingface.co/saheedniyi/YarnGPT/tree/main/audio).
+
+## Model Details
+
+- Base: [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M)
+- Training: 5 epochs on A100 GPU
+- Data: Nigerian movies, podcasts, and open-source audio
+- Architecture: Pure language modeling approach
+
+## Limitations
+
+- English to Nigerian-accented English only
+- May not capture all Nigerian accent variations
+- Training data includes auto-generated content
+
+## Citation
+
+```bibtex
+@misc{yarngpt2025,
+  author = {Saheed Azeez},
+  title = {YarnGPT: Nigerian-Accented English Text-to-Speech Model},
+  year = {2025},
+  publisher = {Hugging Face}
+}
+```
+
+## License
+MIT
+
+## Acknowledgments
+Built with [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) and inspired by [OuteTTS](https://huggingface.co/OuteAI/OuteTTS-0.2-500M/).

yarngpt/__init__.py

@@ -0,0 +1 @@
+

yarngpt/audiotokenizer.py

@@ -0,0 +1,319 @@
+import os
+import re
+import json
+import torch
+import inflect
+import random
+import uroman as ur
+import numpy as np
+import torchaudio
+from transformers import AutoTokenizer
+from outetts.wav_tokenizer.decoder import WavTokenizer
+from outetts.wav_tokenizer.encoder.utils import convert_audio
+
+class AudioTokenizer:
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.bos = "<|im_start|>"
+        self.eos = "<|im_end|>"
+        self.input_length=0
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>"
+        }
+        self.lec = inflect.engine()
+        #self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        #self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+        #self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"
+        self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)
+        self.wavtokenizer = self.wavtokenizer.to(self.device)
+        self.BASE_DIR = os.path.dirname(__file__)
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"]
+
+    def get_speaker_path(self,speaker_name):
+        return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name)
+        return self.load_speaker(speaker_path)
+
+
+    def process_text(self, text: str):
+
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_audio_prompt(self,words: list) -> str:
+        prompt = []
+        for i in words:
+            word = i["word"]
+            duration = self.special_tokens["time"].format(float(i["duration"]))
+            tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]])
+            prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}')
+        return "\n".join(prompt)
+
+    def create_prompt(self,text,speaker_name="idera"):
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+    def tokenize_prompt(self, prompt):
+        input_ids = self.tokenizer.encode(
+            prompt,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to(self.device)
+        self.input_length=input_ids.shape[1]
+        return input_ids.to(self.device)
+
+
+    def get_audio(self,discrete_code):
+        discrete_code=torch.tensor([[discrete_code]]).to(self.device)
+        features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)
+        bandwidth_id = torch.tensor([0]).to(self.device)
+        audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)
+        return audio_out.to("cpu")
+
+    def extract_integers(self,s):
+        # Match integers enclosed in vertical bars |integer|
+        matches = re.findall(r'\|(-?\d+)\|', s)
+        # Convert matches to integers
+        return [int(match) for match in matches]
+
+    def get_codes(self, output):
+        new_output=self.tokenizer.decode(output[0][self.input_length:])
+        codes=self.extract_integers(new_output)
+        return codes
+
+
+class AudioTokenizerForLocal(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.speakers = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']"
+        #if no speaker
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            else:
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+
+class AudioTokenizerV2(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+            "english":"<|english|>",#<|english|>
+            "asr":"<|asr|>"
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers_local = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"]
+        self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),
+                             ('<|1837|>', '<|1836|><|1836|>'),
+                             ('<|1838|>', '<|1837|><|1837|>'),
+                             ('<|1840|>', '<|244|><|167|>'),
+                             ('<|1841|>', '<|235|><|219|>'),
+                             ('<|1844|>', '<|453|><|244|>'),
+                             ('<|1845|>', '<|1838|><|1838|>')]
+
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def get_speaker_path(self,speaker_name,dir):
+        return os.path.join(dir, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str,dir: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name,dir)
+        return self.load_speaker(speaker_path)
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']"
+        #if no speaker
+        dir=self.DEFAULT_SPEAKERS_DIR_LOCAL
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            elif lang=="yoruba":
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+            else:
+                speaker_name=random.choice(self.speakers_eng)
+                
+        if lang=="english":
+            dir=self.DEFAULT_SPEAKERS_ENG
+        speaker=self.load_default_speaker(speaker_name,dir)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+    def replace_tokens(text):
+      for pair in self.changed_tokens:
+        text=text.replace(pair[0],pair[-1])
+      return text 
+
+    def resample(self,audio: np.ndarray, sr: int, target_sr: int):
+        audio = audio.to(dtype=torch.float32)
+        #.clone().detach()
+        audio = audio.unsqueeze(0)
+        # 1 as last arg corresponds to mono audio
+        resampled = convert_audio(audio, sr, target_sr, 1)
+        return resampled.to(self.device )
+
+    def quantize_wavtokenizer(self, path):
+        audio_data, sample_rate = torchaudio.load(path)
+        audio_data=audio_data.squeeze()
+        audio = self.resample(audio_data, sample_rate, 24000).to(self.device)
+        if audio.ndim==3:
+            audio=audio.squeeze(1)
+        bandwidth_id = torch.tensor([0]).to(self.device )
+        _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)
+        codes = codes.squeeze(1).to(self.device)#+last_text_token
+        res=""
+        for code in codes[0].tolist():
+            res+=f"<|{code}|>"
+        return res
+        
+    def create_asr_prompt(self,audio_path):
+        codes=self.quantize_wavtokenizer(audio_path)
+        prompt = self.asr_prompt.format(
+          bos=self.bos,
+          code_start=self.special_tokens['code_start'],
+          codes=codes,
+          code_end=self.special_tokens['code_end'],
+          asr=self.special_tokens["asr"],
+        )
+        return prompt
+
+    def get_asr_results(self,output):
+        res=""
+        for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"):
+            res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0]
+            res+=" "
+        return res.strip()

yarngpt/default_speakers/azeez.json

@@ -0,0 +1,413 @@
+{
+    "text": "Hello! My name is Saheed azeez and I am testing the audio feature",
+    "words": [
+        {
+            "word": "hello",
+            "duration": 1.22,
+            "codes": [
+                219,
+                244,
+                244,
+                167,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                453,
+                244,
+                219,
+                237,
+                864,
+                1041,
+                1048,
+                1372,
+                1780,
+                1554,
+                1024,
+                702,
+                1814,
+                1754,
+                1315,
+                1697,
+                1719,
+                1682,
+                307,
+                621,
+                901,
+                355,
+                783,
+                1726,
+                353,
+                1416,
+                729,
+                803,
+                1494,
+                353,
+                876,
+                1818,
+                932,
+                1068,
+                1813,
+                875,
+                1774,
+                766,
+                1453,
+                1466,
+                792,
+                1388,
+                1495,
+                1236,
+                1462,
+                431,
+                1025,
+                1429,
+                1128,
+                1236,
+                1483,
+                1305,
+                1352,
+                1681,
+                5,
+                1758,
+                1481,
+                1339
+            ]
+        },
+        {
+            "word": "my",
+            "duration": 0.18,
+            "codes": [
+                1333,
+                1339,
+                1388,
+                1373,
+                974,
+                723,
+                1776,
+                1001,
+                1160,
+                1769,
+                1048,
+                1646,
+                1321,
+                912
+            ]
+        },
+        {
+            "word": "name",
+            "duration": 0.2,
+            "codes": [
+                1596,
+                325,
+                876,
+                1303,
+                973,
+                1707,
+                1332,
+                1300,
+                145,
+                1136,
+                1266,
+                1353,
+                845,
+                913,
+                989
+            ]
+        },
+        {
+            "word": "is",
+            "duration": 0.12,
+            "codes": [
+                1257,
+                1372,
+                1617,
+                1800,
+                1568,
+                1679,
+                1798,
+                1476,
+                1759
+            ]
+        },
+        {
+            "word": "saheed",
+            "duration": 0.5,
+            "codes": [
+                1807,
+                1354,
+                1737,
+                1738,
+                1060,
+                1122,
+                1195,
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yarngpt/default_speakers/chinenye.json

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yarngpt/default_speakers/emma.json

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yarngpt/default_speakers/idera.json

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yarngpt/default_speakers/joke.json

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yarngpt/default_speakers/jude.json

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yarngpt/default_speakers/onye.json

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yarngpt/default_speakers/osagie.json

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yarngpt/default_speakers/regina.json

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yarngpt/default_speakers/remi.json

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yarngpt/default_speakers/saheed.json

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yarngpt/default_speakers/tayo.json

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yarngpt/default_speakers/umar.json

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yarngpt/default_speakers/zainab.json

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+}

yarngpt/default_speakers_local/hausa_female1.json

@@ -0,0 +1,273 @@
+{
+    "text": "Idan hira tayi \u0257a\u0257i bana son na tashi.",
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+}

yarngpt/default_speakers_local/hausa_female2.json

@@ -0,0 +1,273 @@
+{
+    "text": "Idan hira tayi \u0257a\u0257i bana son na tashi.",
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+    ]
+}

yarngpt/default_speakers_local/hausa_male1.json

@@ -0,0 +1,367 @@
+{
+    "text": "Eh, mun za\u0253i yin wasan kwaikwayo don nuna al'adunmu yayin ranar al'ada.",
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+        }
+    ]
+}

yarngpt/default_speakers_local/hausa_male2.json

@@ -0,0 +1,207 @@
+{
+    "text": "Audu ya hau jirgi a Kaduna.",
+    "words": [
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+        }
+    ]
+}

yarngpt/default_speakers_local/igbo_female1.json

@@ -0,0 +1,246 @@
+{
+    "text": "Codeine na-agba ah\u1ee5 \u1ecbnweta.",
+    "words": [
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+}

yarngpt/default_speakers_local/igbo_female2.json

@@ -0,0 +1,202 @@
+{
+    "text": "Umunwoke n\u1ecd na \u1ecct\u1ee5t\u1ee5 \u1ecdr\u1ee5 \u1ecdch\u1ecbch\u1ecb",
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+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/igbo_male2.json

@@ -0,0 +1,277 @@
+{
+    "text": "Any\u1ecb na-eji nkw\u1ee5 n'ihu na-eme fan aka",
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+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_female1.json

@@ -0,0 +1,416 @@
+{
+    "text": "Kulikuli j\u1eb9\u0301 \u01f9kan \u00ecpanu t\u00ed w\u00f3\u0323n \u1e63e n\u00edpa l\u00edlo \u1eb9\u0300p\u00e0, p\u1eb9lu or\u00eds\u00ec\u00edr\u00eds\u00ec\u00ed \u01f9kan",
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+        {
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+            ]
+        }
+    ]
+}

yarngpt/default_speakers_local/yoruba_female2.json

@@ -0,0 +1,193 @@
+{
+    "text": "Irin\u1e63\u1eb9\u0301 \u00e0gb\u1eb9\u0300 ni katakata.",
+    "words": [
+        {
+            "word": "irinse",
+            "duration": "1.19",
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yarngpt/default_speakers_local/yoruba_male1.json

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yarngpt/default_speakers_local/yoruba_male2.json

@@ -0,0 +1,238 @@
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+}

yarngpt/default_speakers_local/yoruba_male3.json

@@ -0,0 +1,234 @@
+{
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+}

yarngpt/notebooks/Merge_datasets.ipynb

@@ -0,0 +1,851 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mKb-4Hv4xNpF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8f45fbf9-5e31-4995-a18b-b5d2b9a9e9f5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting datasets\n",
+            "  Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
+            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
+            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
+            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.67.1)\n",
+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+            "Collecting multiprocess<0.70.17 (from datasets)\n",
+            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
+            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
+            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.10)\n",
+            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.27.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.2)\n",
+            "Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.3.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+            "Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
+            "  Attempting uninstall: fsspec\n",
+            "    Found existing installation: fsspec 2024.10.0\n",
+            "    Uninstalling fsspec-2024.10.0:\n",
+            "      Successfully uninstalled fsspec-2024.10.0\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-Oz-lmmExH_F"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "import huggingface_hub\n",
+        "import datasets\n",
+        "from datasets import load_dataset, load_from_disk,concatenate_datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "all_df=[]\n",
+        "for df_path in os.listdir(\"/content/drive/MyDrive/Tokenized\"):\n",
+        "  if ('.gsheet' not in df_path) and ((\"yt\" in df_path) or (\"mv\" in df_path)):\n",
+        "    print(df_path)\n",
+        "    all_df.append(  pd.read_csv(f\"/content/drive/MyDrive/Tokenized/{df_path}\"))\n",
+        "  #print(df_path)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ACU3ZWjAfTsI",
+        "outputId": "d57c0bd1-6161-4ef3-f722-480e1f40af05"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tokenized_yt0.csv\n",
+            "tokenized_mv0.csv\n",
+            "tokenized_yt1.csv\n",
+            "tokenized_mv1.csv\n",
+            "tokenized_yt2.csv\n",
+            "tokenized_mv2.csv\n",
+            "tokenized_yt3.csv\n",
+            "tokenized_mv3.csv\n",
+            "tokenized_yt4.csv\n",
+            "tokenized_mv4.csv\n",
+            "tokenized_yt5.csv\n",
+            "tokenized_mv5.csv\n",
+            "tokenized_yt6.csv\n",
+            "tokenized_mv6.csv\n",
+            "tokenized_yt7.csv\n",
+            "tokenized_mv7.csv\n",
+            "tokenized_yt8.csv\n",
+            "tokenized_mv8.csv\n",
+            "tokenized_yt9.csv\n",
+            "tokenized_mv9.csv\n",
+            "tokenized_yt10.csv\n",
+            "tokenized_mv10.csv\n",
+            "tokenized_yt11.csv\n",
+            "tokenized_mv11.csv\n",
+            "tokenized_yt12.csv\n",
+            "tokenized_mv12.csv\n",
+            "tokenized_yt13.csv\n",
+            "tokenized_mv13.csv\n",
+            "tokenized_yt14.csv\n",
+            "tokenized_mv14.csv\n",
+            "tokenized_yt15.csv\n",
+            "tokenized_mv15.csv\n",
+            "tokenized_yt16.csv\n",
+            "tokenized_mv16.csv\n",
+            "tokenized_yt17.csv\n",
+            "tokenized_mv17.csv\n",
+            "tokenized_yt18.csv\n",
+            "tokenized_mv18.csv\n",
+            "tokenized_yt19.csv\n",
+            "tokenized_mv19.csv\n",
+            "tokenized_yt20.csv\n",
+            "tokenized_mv20.csv\n",
+            "tokenized_yt21.csv\n",
+            "tokenized_mv21.csv\n",
+            "tokenized_yt22.csv\n",
+            "tokenized_mv22.csv\n",
+            "tokenized_yt23.csv\n",
+            "tokenized_mv23.csv\n",
+            "tokenized_yt24.csv\n",
+            "tokenized_mv24.csv\n",
+            "tokenized_yt25.csv\n",
+            "tokenized_mv25.csv\n",
+            "tokenized_yt26.csv\n",
+            "tokenized_mv26.csv\n",
+            "tokenized_mv27.csv\n",
+            "tokenized_yt27.csv\n",
+            "tokenized_mv28.csv\n",
+            "tokenized_yt28.csv\n",
+            "tokenized_mv29.csv\n",
+            "tokenized_yt29.csv\n",
+            "tokenized_mv30.csv\n",
+            "tokenized_yt30.csv\n",
+            "tokenized_mv31.csv\n",
+            "tokenized_yt31.csv\n",
+            "tokenized_mv32.csv\n",
+            "tokenized_yt32.csv\n",
+            "tokenized_mv33.csv\n",
+            "tokenized_yt33.csv\n",
+            "tokenized_mv34.csv\n",
+            "tokenized_yt34.csv\n",
+            "tokenized_mv35.csv\n",
+            "tokenized_mv37.csv\n",
+            "tokenized_mv38.csv\n",
+            "tokenized_yt37.csv\n",
+            "tokenized_mv39.csv\n",
+            "tokenized_yt38.csv\n",
+            "tokenized_mv40.csv\n",
+            "tokenized_yt39.csv\n",
+            "tokenized_mv41.csv\n",
+            "tokenized_yt40.csv\n",
+            "tokenized_mv42.csv\n",
+            "tokenized_yt41.csv\n",
+            "tokenized_mv43.csv\n",
+            "tokenized_yt42.csv\n",
+            "tokenized_mv44.csv\n",
+            "tokenized_yt43.csv\n",
+            "tokenized_mv45.csv\n",
+            "tokenized_mv46.csv\n",
+            "tokenized_yt44.csv\n",
+            "tokenized_mv47.csv\n",
+            "tokenized_yt45.csv\n",
+            "tokenized_mv48.csv\n",
+            "tokenized_yt46.csv\n",
+            "tokenized_mv49.csv\n",
+            "tokenized_yt47.csv\n",
+            "tokenized_mv50.csv\n",
+            "tokenized_yt48.csv\n",
+            "tokenized_mv51.csv\n",
+            "tokenized_mv70.csv\n",
+            "tokenized_yt70.csv\n",
+            "tokenized_mv71.csv\n",
+            "tokenized_mv72.csv\n",
+            "tokenized_yt71.csv\n",
+            "tokenized_mv73.csv\n",
+            "tokenized_yt72.csv\n",
+            "tokenized_mv74.csv\n",
+            "tokenized_yt73.csv\n",
+            "tokenized_mv75.csv\n",
+            "tokenized_yt74.csv\n",
+            "tokenized_mv76.csv\n",
+            "tokenized_yt75.csv\n",
+            "tokenized_mv77.csv\n",
+            "tokenized_yt76.csv\n",
+            "tokenized_mv78.csv\n",
+            "tokenized_yt77.csv\n",
+            "tokenized_mv79.csv\n",
+            "tokenized_yt78.csv\n",
+            "tokenized_mv80.csv\n",
+            "tokenized_mv81.csv\n",
+            "tokenized_yt79.csv\n",
+            "tokenized_mv82.csv\n",
+            "tokenized_yt80.csv\n",
+            "tokenized_mv83.csv\n",
+            "tokenized_yt81.csv\n",
+            "tokenized_mv84.csv\n",
+            "tokenized_yt82.csv\n",
+            "tokenized_mv85.csv\n",
+            "tokenized_yt83.csv\n",
+            "tokenized_mv86.csv\n",
+            "tokenized_yt84.csv\n",
+            "tokenized_mv87.csv\n",
+            "tokenized_yt85.csv\n",
+            "tokenized_mv88.csv\n",
+            "tokenized_yt86.csv\n",
+            "tokenized_mv89.csv\n",
+            "tokenized_mv90.csv\n",
+            "tokenized_yt87.csv\n",
+            "tokenized_mv91.csv\n",
+            "tokenized_yt88.csv\n",
+            "tokenized_mv92.csv\n",
+            "tokenized_yt89.csv\n",
+            "tokenized_mv93.csv\n",
+            "tokenized_yt90.csv\n",
+            "tokenized_mv94.csv\n",
+            "tokenized_mv95.csv\n",
+            "tokenized_yt91.csv\n",
+            "tokenized_mv96.csv\n",
+            "tokenized_yt92.csv\n",
+            "tokenized_mv97.csv\n",
+            "tokenized_yt93.csv\n",
+            "tokenized_mv98.csv\n",
+            "tokenized_yt94.csv\n",
+            "tokenized_mv99.csv\n",
+            "tokenized_mv100.csv\n",
+            "tokenized_yt95.csv\n",
+            "tokenized_mv101.csv\n",
+            "tokenized_yt96.csv\n",
+            "tokenized_mv102.csv\n",
+            "tokenized_yt97.csv\n",
+            "tokenized_mv103.csv\n",
+            "tokenized_yt98.csv\n",
+            "tokenized_mv104.csv\n",
+            "tokenized_mv105.csv\n",
+            "tokenized_yt99.csv\n",
+            "tokenized_mv106.csv\n",
+            "tokenized_yt100.csv\n",
+            "tokenized_mv107.csv\n",
+            "tokenized_yt101.csv\n",
+            "tokenized_mv108.csv\n",
+            "tokenized_yt102.csv\n",
+            "tokenized_mv109.csv\n",
+            "tokenized_mv110.csv\n",
+            "tokenized_yt103.csv\n",
+            "tokenized_mv112.csv\n",
+            "tokenized_yt104.csv\n",
+            "tokenized_yt105.csv\n",
+            "tokenized_yt106.csv\n",
+            "tokenized_yt107.csv\n",
+            "tokenized_yt108.csv\n",
+            "tokenized_yt109.csv\n",
+            "tokenized_yt110.csv\n",
+            "tokenized_yt111.csv\n",
+            "tokenized_yt112.csv\n",
+            "tokenized_yt113.csv\n",
+            "tokenized_yt114.csv\n",
+            "tokenized_yt115.csv\n",
+            "tokenized_yt116.csv\n",
+            "tokenized_yt117.csv\n",
+            "tokenized_yt118.csv\n",
+            "tokenized_yt119.csv\n",
+            "tokenized_yt120.csv\n",
+            "tokenized_yt121.csv\n",
+            "tokenized_yt122.csv\n",
+            "tokenized_yt123.csv\n",
+            "tokenized_yt124.csv\n",
+            "tokenized_yt125.csv\n",
+            "tokenized_yt126.csv\n",
+            "tokenized_yt127.csv\n",
+            "tokenized_yt128.csv\n",
+            "tokenized_yt129.csv\n",
+            "tokenized_yt130.csv\n",
+            "tokenized_yt131.csv\n",
+            "tokenized_yt132.csv\n",
+            "tokenized_yt133.csv\n",
+            "tokenized_yt134.csv\n",
+            "tokenized_yt135.csv\n",
+            "tokenized_yt136.csv\n",
+            "tokenized_yt137.csv\n",
+            "tokenized_yt138.csv\n",
+            "tokenized_yt139.csv\n",
+            "tokenized_yt140.csv\n",
+            "tokenized_yt141.csv\n",
+            "tokenized_yt142.csv\n",
+            "tokenized_yt143.csv\n",
+            "tokenized_yt144.csv\n",
+            "tokenized_yt145.csv\n",
+            "tokenized_yt146.csv\n",
+            "tokenized_yt147.csv\n",
+            "tokenized_yt148.csv\n",
+            "tokenized_yt149.csv\n",
+            "tokenized_yt150.csv\n",
+            "tokenized_yt151.csv\n",
+            "tokenized_yt152.csv\n",
+            "tokenized_yt153.csv\n",
+            "tokenized_yt154.csv\n",
+            "tokenized_yt155.csv\n",
+            "tokenized_yt156.csv\n",
+            "tokenized_yt157.csv\n",
+            "tokenized_yt158.csv\n",
+            "tokenized_yt159.csv\n",
+            "tokenized_yt160.csv\n",
+            "tokenized_yt161.csv\n",
+            "tokenized_yt162.csv\n",
+            "tokenized_yt163.csv\n",
+            "tokenized_yt164.csv\n",
+            "tokenized_yt165.csv\n",
+            "tokenized_yt166.csv\n",
+            "tokenized_yt167.csv\n",
+            "tokenized_yt168.csv\n",
+            "tokenized_yt169.csv\n",
+            "tokenized_yt170.csv\n",
+            "tokenized_yt171.csv\n",
+            "tokenized_yt172.csv\n",
+            "tokenized_yt173.csv\n",
+            "tokenized_yt174.csv\n",
+            "tokenized_yt175.csv\n",
+            "tokenized_yt176.csv\n",
+            "tokenized_yt177.csv\n",
+            "tokenized_yt178.csv\n",
+            "tokenized_yt179.csv\n",
+            "tokenized_yt180.csv\n",
+            "tokenized_yt181.csv\n",
+            "tokenized_yt182.csv\n",
+            "tokenized_yt183.csv\n",
+            "tokenized_yt184.csv\n",
+            "tokenized_yt185.csv\n",
+            "tokenized_yt186.csv\n",
+            "tokenized_yt187.csv\n",
+            "tokenized_yt188.csv\n",
+            "tokenized_yt189.csv\n",
+            "tokenized_yt190.csv\n",
+            "tokenized_yt191.csv\n",
+            "tokenized_yt192.csv\n",
+            "tokenized_yt193.csv\n",
+            "tokenized_yt194.csv\n",
+            "tokenized_yt195.csv\n",
+            "tokenized_yt196.csv\n",
+            "tokenized_yt197.csv\n",
+            "tokenized_yt198.csv\n",
+            "tokenized_yt199.csv\n",
+            "tokenized_yt200.csv\n",
+            "tokenized_yt201.csv\n",
+            "tokenized_yt202.csv\n",
+            "tokenized_yt203.csv\n",
+            "tokenized_yt204.csv\n",
+            "tokenized_yt205.csv\n",
+            "tokenized_yt206.csv\n",
+            "tokenized_yt210.csv\n",
+            "tokenized_yt211.csv\n",
+            "tokenized_yt212.csv\n",
+            "tokenized_yt213.csv\n",
+            "tokenized_yt214.csv\n",
+            "tokenized_yt215.csv\n",
+            "tokenized_yt216.csv\n",
+            "tokenized_yt217.csv\n",
+            "tokenized_yt218.csv\n",
+            "tokenized_yt219.csv\n",
+            "tokenized_yt220.csv\n",
+            "tokenized_yt221.csv\n",
+            "tokenized_yt222.csv\n",
+            "tokenized_yt223.csv\n",
+            "tokenized_yt224.csv\n",
+            "tokenized_yt225.csv\n",
+            "tokenized_yt226.csv\n",
+            "tokenized_yt227.csv\n",
+            "tokenized_yt228.csv\n",
+            "tokenized_yt230.csv\n",
+            "tokenized_yt1101.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data=pd.concat(all_df)"
+      ],
+      "metadata": {
+        "id": "9f8aFlgOfb6c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "id": "Hsq8k7WogC_j",
+        "outputId": "be6ce9a2-b1a0-4a7b-9251-40bc6e1e538f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Unnamed: 0                                                  0\n",
+              "0            0  <|im_start|>\\n<|text_start|>music<|text_sep|>h...\n",
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+              "4            4  <|im_start|>\\n<|text_start|>um<|text_sep|>im<|...\n",
+              "..         ...                                                ...\n",
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+              "213        213  <|im_start|>\\n<|text_start|>have<|text_sep|>be...\n",
+              "\n",
+              "[295292 rows x 2 columns]"
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+              "\n",
+              "  <div id=\"df-b8e001d3-2f9d-47f0-b7d3-3e4be94d8818\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
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+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;money&lt;|text_sep|&gt;d...</td>\n",
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+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;you&lt;|text_sep|&gt;no&lt;...</td>\n",
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+              "      <th>3</th>\n",
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+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;morning&lt;|text_sep|...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
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+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;um&lt;|text_sep|&gt;im&lt;|...</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>210</th>\n",
+              "      <td>210</td>\n",
+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;im&lt;|text_sep|&gt;look...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>211</th>\n",
+              "      <td>211</td>\n",
+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;all&lt;|text_sep|&gt;of&lt;...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>212</th>\n",
+              "      <td>212</td>\n",
+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;good&lt;|text_sep|&gt;ti...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>213</th>\n",
+              "      <td>213</td>\n",
+              "      <td>&lt;|im_start|&gt;\\n&lt;|text_start|&gt;have&lt;|text_sep|&gt;be...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>295292 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8e001d3-2f9d-47f0-b7d3-3e4be94d8818')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b8e001d3-2f9d-47f0-b7d3-3e4be94d8818 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b8e001d3-2f9d-47f0-b7d3-3e4be94d8818');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a078f78c-88de-41c2-a0a4-09f4550f52fb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a078f78c-88de-41c2-a0a4-09f4550f52fb')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a078f78c-88de-41c2-a0a4-09f4550f52fb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0bd96b93-aa89-458f-a577-12a9d983d031\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0bd96b93-aa89-458f-a577-12a9d983d031 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('train_data');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "train_data"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.to_csv(\"/content/drive/MyDrive/Tokenized2/all_data.csv\")"
+      ],
+      "metadata": {
+        "id": "S0011JRDtLO2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

yarngpt/notebooks/Merge_datasets_local (1).ipynb

@@ -0,0 +1,258 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mKb-4Hv4xNpF"
+      },
+      "outputs": [],
+      "source": [
+        "pip install datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1QHW2w8cdupP"
+      },
+      "outputs": [],
+      "source": [
+        "import huggingface_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-Oz-lmmExH_F"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "import huggingface_hub\n",
+        "import datasets\n",
+        "from datasets import load_dataset, load_from_disk,concatenate_datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NfVKJ5xgdyc1"
+      },
+      "outputs": [],
+      "source": [
+        "huggingface_hub.login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "llvhheVWjDwi"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "checkpoint=\"saheedniyi/YarnGPT\"\n",
+        "#checkpoint=\"saheedniyi/public_extra2\"#device = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n",
+        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sfVhIZEpisZg"
+      },
+      "outputs": [],
+      "source": [
+        "def token_length(prompt):\n",
+        "     return len(tokenizer(prompt)[\"input_ids\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ACU3ZWjAfTsI"
+      },
+      "outputs": [],
+      "source": [
+        "all_df=[]\n",
+        "for df_path in os.listdir(\"/content/drive/MyDrive/naij_tokenized\"):\n",
+        "  if ('.gsheet' not in df_path):\n",
+        "    df=pd.read_csv(f\"/content/drive/MyDrive/naij_tokenized/{df_path}\")\n",
+        "    df[\"length\"]=df[\"tts\"].apply(token_length)\n",
+        "    print(df_path)\n",
+        "    all_df.append(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9f8aFlgOfb6c"
+      },
+      "outputs": [],
+      "source": [
+        "train_data=pd.concat(all_df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hsq8k7WogC_j"
+      },
+      "outputs": [],
+      "source": [
+        "train_data_1=train_data.drop_duplicates(\"tts\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dVYrYSISn9hE"
+      },
+      "outputs": [],
+      "source": [
+        "train_data_1.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bec2mRTdoAf_"
+      },
+      "outputs": [],
+      "source": [
+        "train_data.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1.drop(\"stt\",axis=1, inplace=True)"
+      ],
+      "metadata": {
+        "id": "pythz_XcgF9t"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1.drop([\"Unnamed: 0\",\"__index_level_0__\"],axis=1, inplace=True)"
+      ],
+      "metadata": {
+        "id": "9yebXiwvgZMa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def replace_text(txt):\n",
+        "    txt=txt.replace(\"<|hausa|\\n\",\"<|hausa|>\\n\")\n",
+        "    txt=txt.replace(\"<|igbo|\\n\",\"<|igbo|>\\n\")\n",
+        "    txt=txt.replace(\"<|yoruba|\\n\",\"<|yoruba|>\\n\")#hausa\":\"<|hausa|\",\n",
+        "    txt=txt.replace(\"\\n<|tts|>\",\"\")\n",
+        "    return txt"
+      ],
+      "metadata": {
+        "id": "kUvi1ItQg6HT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TgWv4W8Wiwd1"
+      },
+      "outputs": [],
+      "source": [
+        "train_data_1=train_data_1[train_data_1[\"tts\"]!=\"An error occurred\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1[\"tts\"]=train_data_1[\"tts\"].apply(replace_text)"
+      ],
+      "metadata": {
+        "id": "CP7bVwRqh_qH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1.shape"
+      ],
+      "metadata": {
+        "id": "YMk40ZsIrW44"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1"
+      ],
+      "metadata": {
+        "id": "RVD3f0frrcfi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1=train_data_1[train_data_1[\"length\"]<4000]"
+      ],
+      "metadata": {
+        "id": "utJLIf1orhnE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_1.to_csv(\"/content/drive/MyDrive/naij_tokenized/final_all_lang.csv\")"
+      ],
+      "metadata": {
+        "id": "BOynFjQlruWJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

yarngpt/python-wrapper/README.md

@@ -0,0 +1,80 @@
+# YarnGPT Python Wrapper Library
+
+## Description
+YarnGPT is a Python wrapper for the YarnGPT text-to-speech model, designed to synthesize natural Nigerian-accented English speech using a pure language modeling approach. This library provides a simple API to convert text into audio output, allowing users to select from various preset voices and adjust generation parameters.
+
+## Features
+- Supports 6 preset voices (idera, jude, joke, umar, osagie, onye)
+- Utilizes Hugging Face's model caching for efficient model loading
+- Exposes a straightforward API function: generate_speech(text, speaker, temperature, repetition_penalty, max_length)
+- Allows customization of generation parameters such as temperature, repetition penalty, and maximum token length
+- Includes unit tests to ensure core functionality
+
+## Installation
+1. Create and activate a virtual environment:
+   - On Linux/MacOS:
+   ```bash
+   python3 -m venv env
+   source env/bin/activate
+   ```
+   - On Windows:
+   ```bash
+   python -m venv env
+   env\Scripts\activate
+   ```
+
+2. Install the package:
+   ```bash
+   pip install yarngpt
+   ```
+
+## Usage
+Basic usage to generate and save audio:
+```python
+from yarngpt import generate_speech
+import torchaudio
+
+# Generate speech with the default speaker (idera)
+audio = generate_speech("Hello, this is a test.")
+
+# Save the generated audio
+torchaudio.save("output.wav", audio, sample_rate=24000)
+```
+
+For Jupyter Notebook users, you can also play the audio directly:
+```python
+from yarngpt import generate_speech
+import torchaudio
+from IPython.display import Audio
+
+# Generate and save speech
+audio = generate_speech("Hello, this is a test.", speaker="joke")
+torchaudio.save("output.wav", audio, sample_rate=24000)
+
+# Play the audio in the notebook
+Audio("output.wav")
+```
+
+## Parameter Options
+- `text`: The input string to convert to speech
+- `speaker`: Choose from available speakers: idera, jude, joke, umar, osagie, onye (default is "idera")
+- `temperature`: Controls the randomness of generation (default is 0.1)
+- `repetition_penalty`: A factor to reduce repetitive output (default is 1.1)
+- `max_length`: The maximum length of the generated output tokens (default is 4000)
+
+## Testing
+Run the unit tests to verify functionality:
+```bash
+python -m unittest discover -s tests
+```
+
+
+## License
+This project is licensed under the MIT License.
+
+## Acknowledgments
+- Built as a contribution to yarngpt projects
+- Utilizes Hugging Face's model caching and the transformers library
+- Special thanks to the open-source community for their ongoing support
+
+For more details and documentation, visit the GitHub repository: https://github.com/jerryola1

yarngpt/python-wrapper/audiotokenizer.py

@@ -0,0 +1,317 @@
+import os
+import re
+import json
+import torch
+import inflect
+import random
+import uroman as ur
+import numpy as np
+import torchaudio
+from transformers import AutoTokenizer
+from outetts.wav_tokenizer.decoder import WavTokenizer
+from outetts.wav_tokenizer.encoder.utils import convert_audio
+
+class AudioTokenizer:
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.bos = "<|im_start|>"
+        self.eos = "<|im_end|>"
+        self.input_length=0
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>"
+        }
+        self.lec = inflect.engine()
+        #self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{audio_start}\n"
+        #self.config_path = "/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+        #self.model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"
+        self.wavtokenizer = WavTokenizer.from_pretrained0802(wav_tokenizer_config_path, wav_tokenizer_model_path)
+        self.wavtokenizer = self.wavtokenizer.to(self.device)
+        self.BASE_DIR = os.path.dirname(__file__)
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers=["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye"]
+
+    def get_speaker_path(self,speaker_name):
+        return os.path.join(self.DEFAULT_SPEAKERS_DIR, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name)
+        return self.load_speaker(speaker_path)
+
+
+    def process_text(self, text: str):
+
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_audio_prompt(self,words: list) -> str:
+        prompt = []
+        for i in words:
+            word = i["word"]
+            duration = self.special_tokens["time"].format(float(i["duration"]))
+            tokens = "".join([self.special_tokens["audio_code"].format(c) for c in i["codes"]])
+            prompt.append(f'{word}{duration}{self.special_tokens["code_start"]}{tokens}{self.special_tokens["code_end"]}')
+        return "\n".join(prompt)
+
+    def create_prompt(self,text,speaker_name="idera"):
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+    def tokenize_prompt(self, prompt):
+        input_ids = self.tokenizer.encode(
+            prompt,
+            add_special_tokens=False,
+            return_tensors="pt"
+        ).to(self.device)
+        self.input_length=input_ids.shape[1]
+        return input_ids.to(self.device)
+
+
+    def get_audio(self,discrete_code):
+        discrete_code=torch.tensor([[discrete_code]]).to(self.device)
+        features = self.wavtokenizer.codes_to_features(discrete_code).to(self.device)
+        bandwidth_id = torch.tensor([0]).to(self.device)
+        audio_out = self.wavtokenizer.decode(features, bandwidth_id=bandwidth_id)
+        return audio_out.to("cpu")
+
+    def extract_integers(self,s):
+        # Match integers enclosed in vertical bars |integer|
+        matches = re.findall(r'\|(-?\d+)\|', s)
+        # Convert matches to integers
+        return [int(match) for match in matches]
+
+    def get_codes(self, output):
+        new_output=self.tokenizer.decode(output[0][self.input_length:])
+        codes=self.extract_integers(new_output)
+        return codes
+
+
+class AudioTokenizerForLocal(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.speakers = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba']"
+        #if no speaker
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            else:
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+        speaker=self.load_default_speaker(speaker_name)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+
+
+class AudioTokenizerV2(AudioTokenizer):
+
+    def __init__(self,tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path,):
+        super().__init__(tokenizer_path, wav_tokenizer_model_path, wav_tokenizer_config_path)
+        self.text_prompt = "{bos}\n{text_start}{words}{text_end}\n{lang}\n{audio_start}\n"
+        self.asr_prompt="{bos}\n{code_start}{codes}{code_end}\n{asr}\n"
+        self.special_tokens = {
+            "audio_code": "<|{}|>",
+            "text_start": "<|text_start|>",
+            "text_end": "<|text_end|>",
+            "audio_start": "<|audio_start|>",
+            "audio_end": "<|audio_end|>",
+            "word_start": "<|word_start|>",
+            "word_end": "<|word_end|>",
+            "time": "<|t_{:.2f}|>",
+            "code_start": "<|code_start|>",
+            "code_end": "<|code_end|>",
+            "text_sep": "<|text_sep|>",
+            "hausa":"<|hausa|>",
+            "igbo":"<|igbo|>",
+            "yoruba":"<|yoruba|>",
+            "english":"<|english|>",#<|english|>
+            "asr":"<|asr|>"
+        }
+        self.uroman = ur.Uroman()
+        self.DEFAULT_SPEAKERS_DIR_LOCAL = os.path.join(self.BASE_DIR, "default_speakers_local")
+        self.DEFAULT_SPEAKERS_ENG = os.path.join(self.BASE_DIR, "default_speakers")
+        self.speakers_local = [
+            "hausa_male1", "hausa_male2","yoruba_male1", "yoruba_male2","igbo_male2" #"igbo_male1", "igbo_male2",
+            "hausa_female1", "hausa_female2", "igbo_female1", "igbo_female2", "yoruba_female1", "yoruba_female2"
+        ]
+        self.speakers_eng = ["idera","emma","onye","jude","osagie","tayo","zainab","joke","regina","remi","umar","chinenye","saheed"]
+        self.changed_tokens=[('<|1836|>', '<|453|><|453|>'),
+                             ('<|1837|>', '<|1836|><|1836|>'),
+                             ('<|1838|>', '<|1837|><|1837|>'),
+                             ('<|1840|>', '<|244|><|167|>'),
+                             ('<|1841|>', '<|235|><|219|>'),
+                             ('<|1844|>', '<|453|><|244|>'),
+                             ('<|1845|>', '<|1838|><|1838|>')]
+
+    def process_text(self, text: str):
+        text = self.uroman.romanize_string(text)
+        text = re.sub(r'\d+(\.\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())
+        text = re.sub(r'[-_/,\.\\]', ' ', text)
+        text = re.sub(r'[^a-z\s]', '', text)
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text.split()
+
+    def get_speaker_path(self,speaker_name,dir):
+        return os.path.join(dir, f"{speaker_name}.json")
+
+    def load_speaker(self, path: str):
+        with open(path, "r") as f:
+            return json.load(f)
+
+    def load_default_speaker(self, name: str,dir: str):
+        name = name.lower().strip()
+        speaker_path=self.get_speaker_path(name,dir)
+        return self.load_speaker(speaker_path)
+
+    def create_prompt(self,text,lang,speaker_name=None):
+        assert lang in ["hausa","igbo","yoruba","english"], f"Invalid language: {lang}, language must be one of ['hausa','igbo','yoruba','english']"
+        #if no speaker
+        dir=self.DEFAULT_SPEAKERS_DIR_LOCAL
+        if speaker_name is None:
+            if lang=="hausa":
+                speaker_name=random.choice(["hausa_male1","hausa_male2","hausa_female1","hausa_female2"])
+            elif lang=="igbo":
+                speaker_name=random.choice(["igbo_female1","igbo_female2","igbo_male2"])#"igbo_male1"])
+            elif lang=="yoruba":
+                speaker_name=random.choice(["yoruba_male2","yoruba_female1","yoruba_female2"])
+            else:
+                speaker_name=random.choice(self.speakers_eng)
+                
+        if lang=="english":
+            dir=self.DEFAULT_SPEAKERS_ENG
+        speaker=self.load_default_speaker(speaker_name,dir)
+        input_words = self.process_text(speaker["text"]) +  self.process_text(text)
+        #input_words = process_text(speaker["text"]) + input_words
+
+        inputs_words_strings = f"{self.special_tokens['text_sep']}".join([i.strip() for i in input_words])
+        prompt = self.text_prompt.format(
+          bos=self.bos,
+          text_start=self.special_tokens['text_start'],
+          words=inputs_words_strings,
+          text_end=self.special_tokens['text_end'],
+          lang=self.special_tokens[lang],
+          audio_start=self.special_tokens['audio_start']
+      )
+        prompt += self.create_audio_prompt(speaker["words"])
+
+        return prompt
+    def replace_tokens(text):
+      for pair in self.changed_tokens:
+        text=text.replace(pair[0],pair[-1])
+      return text 
+
+    def resample(self,audio: np.ndarray, sr: int, target_sr: int):
+        audio = audio.to(dtype=torch.float32)
+        #.clone().detach()
+        audio = audio.unsqueeze(0)
+        # 1 as last arg corresponds to mono audio
+        resampled = convert_audio(audio, sr, target_sr, 1)
+        return resampled.to(self.device )
+
+    def quantize_wavtokenizer(self, path):
+        audio_data, sample_rate = torchaudio.load(path)
+        audio_data=audio_data.squeeze()
+        audio = self.resample(audio_data, sample_rate, 24000).to(self.device)
+        bandwidth_id = torch.tensor([0]).to(self.device )
+        _, codes = self.wavtokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)
+        codes = codes.squeeze(1).to(self.device)#+last_text_token
+        res=""
+        for code in codes[0].tolist():
+            res+=f"<|{code}|>"
+        return res
+        
+    def load_asr_prompt(self,audio_path):
+        codes=self.quantize_wavtokenizer(audio_path)
+        prompt = self.asr_prompt.format(
+          bos=self.bos,
+          code_start=self.special_tokens['code_start'],
+          codes=codes,
+          code_end=self.special_tokens['code_end'],
+          asr=self.special_tokens["asr"],
+        )
+        return prompt
+
+    def get_asr_results(self,output):
+        res=""
+        for text in self.tokenizer.decode(output[0]).split("<|text_start|>")[-1].split("<|text_end|>")[0].split("\n"):
+            res+=text.split("<|word_start|>")[-1].split("<|word_end|>")[0]
+            res+=" "
+        return res.strip()

yarngpt/python-wrapper/default_speakers/.ipynb_checkpoints/emma-checkpoint.json

@@ -0,0 +1,441 @@
+{
+    "text": "Scientists have discovered a new planet that may be capable of supporting life!",
+    "words": [
+        {
+            "word": "scientists",
+            "duration": 0.82,
+            "codes": [
+                1334,
+                1359,
+                619,
+                1057,
+                1528,
+                817,
+                1175,
+                884,
+                527,
+                1519,
+                323,
+                980,
+                608,
+                1104,
+                1271,
+                1265,
+                1237,
+                191,
+                1308,
+                203,
+                1126,
+                1226,
+                1265,
+                1073,
+                1661,
+                903,
+                502,
+                197,
+                127,
+                1712,
+                877,
+                1717,
+                1735,
+                1076,
+                1284,
+                1629,
+                784,
+                62,
+                175,
+                432,
+                767,
+                533,
+                990,
+                1258,
+                823,
+                1651,
+                1801,
+                701,
+                1382,
+                554,
+                527,
+                117,
+                323,
+                989,
+                884,
+                817,
+                495,
+                781,
+                1214,
+                1099,
+                1104
+            ]
+        },
+        {
+            "word": "have",
+            "duration": 0.24,
+            "codes": [
+                930,
+                1393,
+                1303,
+                1001,
+                1438,
+                628,
+                1774,
+                973,
+                1758,
+                1501,
+                1761,
+                1428,
+                1725,
+                669,
+                1780,
+                487,
+                866,
+                1762
+            ]
+        },
+        {
+            "word": "discovered",
+            "duration": 0.66,
+            "codes": [
+                820,
+                1592,
+                1737,
+                731,
+                1325,
+                1644,
+                884,
+                1300,
+                323,
+                596,
+                231,
+                296,
+                943,
+                990,
+                1214,
+                1039,
+                1039,
+                1430,
+                866,
+                19,
+                1675,
+                1824,
+                1030,
+                1630,
+                1758,
+                783,
+                1598,
+                1832,
+                1330,
+                1319,
+                1730,
+                1449,
+                1414,
+                1511,
+                695,
+                1526,
+                1410,
+                95,
+                1686,
+                1400,
+                961,
+                1809,
+                1303,
+                355,
+                544,
+                1671,
+                1493,
+                1290,
+                1732,
+                1808
+            ]
+        },
+        {
+            "word": "a",
+            "duration": 0.14,
+            "codes": [
+                968,
+                1281,
+                895,
+                1827,
+                1819,
+                694,
+                1509,
+                1346,
+                928,
+                1449,
+                1512
+            ]
+        },
+        {
+            "word": "new",
+            "duration": 0.24,
+            "codes": [
+                1433,
+                1689,
+                1685,
+                1598,
+                1547,
+                1369,
+                1228,
+                1708,
+                1285,
+                1722,
+                1257,
+                625,
+                1114,
+                1425,
+                465,
+                950,
+                651,
+                561
+            ]
+        },
+        {
+            "word": "planet",
+            "duration": 0.48,
+            "codes": [
+                1707,
+                821,
+                1225,
+                1228,
+                1168,
+                1291,
+                1739,
+                813,
+                1738,
+                966,
+                1829,
+                1229,
+                1751,
+                1280,
+                1120,
+                1537,
+                1145,
+                1257,
+                1145,
+                1490,
+                1565,
+                41,
+                1677,
+                1796,
+                1258,
+                1228,
+                1389,
+                1145,
+                1433,
+                763,
+                1255,
+                355,
+                509,
+                869,
+                1144,
+                501
+            ]
+        },
+        {
+            "word": "that",
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yarngpt/python-wrapper/default_speakers/.ipynb_checkpoints/onye-checkpoint.json

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yarngpt/python-wrapper/default_speakers/chinenye.json

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yarngpt/python-wrapper/default_speakers/emma.json

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yarngpt/python-wrapper/default_speakers/idera.json

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yarngpt/python-wrapper/default_speakers/joke.json

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yarngpt/python-wrapper/default_speakers/jude.json

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