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import json
import logging
import torchaudio
from torch.utils.data import Dataset
def _handle_wav(wav_path, target_rate=16000):
"""
handle one wav file.
Return:
waveform: numpy narray(1d)
"""
waveform, sample_rate = torchaudio.load(wav_path)
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_rate)(waveform)
audio = waveform[0]
return audio
def _handle_qa(obj, is_think=True, think_max_len=50):
if is_think:
prompt_template = (
"# Dialogue Response Evaluation\n\n"
"**IMPORTANT:** Evaluation must include `<think>` analysis and `<score>` rating.\n\n"
"Listen to the dialogue recording (two sentences, 1-second pause in between). Evaluate the quality of the **second sentence** as a response to the first, focusing on **text relevance** and the **appropriateness** of **Linguistic information (a range of paralinguistic information such as emotion/age/pitch/speed/volume)**.\n"
"**Note:** Focus on evaluating the appropriateness of the second sentence relative to the first, even if the first sentence itself contains contradictory information.\n\n"
"## Scoring Criteria\n\n"
"**1 points**: Text content is irrelevant or incorrect or illogical.(low intelligence)\n"
"**3 points**: Text is relevant, but paralinguistic information is **inappropriate** for the context.(low emotional quotient)\n"
"**5 points**: Text is relevant, and paralinguistic information is **appropriate** for the context, resulting in effective communication.(High intelligence and emotional intelligence.)\n\n"
"## Evaluation Requirements\n\n"
"Response **MUST** follow this format:\n\n"
"<think>\n"
f"Analysing text relevance and paralinguistic information **Appropriateness** and reasons for scoring...(less than {think_max_len} words)\n"
"</think>\n\n"
"<score>X</score> (**X is 1, 3, or 5**)\n\n")
else:
prompt_template = (
"# Dialogue Response Evaluation\n\n"
"**IMPORTANT:** Evaluation must include`<score>` rating.\n\n"
"Listen to the dialogue recording (two sentences, 1-second pause in between). Evaluate the quality of the **second sentence** as a response to the first, focusing on **text relevance** and the **appropriateness** of **Linguistic information (a range of paralinguistic information such as emotion/age/pitch/speed/volume)**.\n"
"**Note:** Focus on evaluating the appropriateness of the second sentence relative to the first, even if the first sentence itself contains contradictory information.\n\n"
"## Scoring Criteria\n\n"
"**1 points**: Text content is irrelevant or incorrect or illogical.(low intelligence)\n"
"**3 points**: Text is relevant, but paralinguistic information is **inappropriate** for the context.(low emotional quotient)\n"
"**5 points**: Text is relevant, and paralinguistic information is **appropriate** for the context, resulting in effective communication.(High intelligence and emotional intelligence.)\n\n"
"## Evaluation Requirements\n\n"
"Response **MUST** follow this format:\n\n"
"<score>X</score> (**X is 1, 3, or 5**)\n\n")
obj["prompt"] = [{"role": "user", "content": [
{"type": "audio", "audio": obj["merge_wav"]},
{"type": "text", "text": prompt_template}
]}]
obj["solution"] = obj["gt_score"]
return obj
class AudioDataset(Dataset):
def __init__(self, data_file, sample_rate=16000, is_think=True, think_max_len=50, load_audio=False):
super().__init__()
with open(data_file, 'r', encoding='utf8') as f:
self.data = json.load(f)
self.sample_rate = sample_rate
self.is_think = is_think
self.think_max_len = think_max_len
self.load_audio = load_audio
logging.info(f"Loaded {len(self.data)} items from {data_file}")
def __len__(self):
return len(self.data)
def __getitem__(self, index):
item = self.data[index]
if self.load_audio:
item["audio"] = _handle_wav(item["merge_wav"], self.sample_rate)
return _handle_qa(
item,
is_think=self.is_think,
think_max_len=self.think_max_len
)
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